Search Results for author: H. Vincent Poor

Found 115 papers, 8 papers with code

Adversarial Neural Networks for Error Correcting Codes

no code implementations21 Dec 2021 Hung T. Nguyen, Steven Bottone, Kwang Taik Kim, Mung Chiang, H. Vincent Poor

To demonstrate the performance of our framework, we combine it with the very recent neural decoders and show improved performance compared to the original models and traditional decoding algorithms on various codes.

Reconfigurable Holographic Surfaces for Future Wireless Communications

no code implementations13 Dec 2021 Ruoqi Deng, Boya Di, Hongliang Zhang, Dusit Niyato, Zhu Han, H. Vincent Poor, Lingyang Song

Future wireless communications look forward to constructing a ubiquitous intelligent information network with high data rates through cost-efficient devices.

BScNets: Block Simplicial Complex Neural Networks

1 code implementation13 Dec 2021 Yuzhou Chen, Yulia R. Gel, H. Vincent Poor

Simplicial neural networks (SNN) have recently emerged as the newest direction in graph learning which expands the idea of convolutional architectures from node space to simplicial complexes on graphs.

Graph Convolutional Network Graph Learning +1

Federated Learning over Wireless IoT Networks with Optimized Communication and Resources

no code implementations22 Oct 2021 Hao Chen, Shaocheng Huang, Deyou Zhang, Ming Xiao, Mikael Skoglund, H. Vincent Poor

Hence, we investigate the problem of jointly optimized communication efficiency and resources for FL over wireless Internet of things (IoT) networks.

Federated Learning

Cooperative Task Offloading and Block Mining in Blockchain-based Edge Computing with Multi-agent Deep Reinforcement Learning

no code implementations29 Sep 2021 Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, H. Vincent Poor

The convergence of mobile edge computing (MEC) and blockchain is transforming the current computing services in mobile networks, by offering task offloading solutions with security enhancement empowered by blockchain mining.


Physical Layer Anonymous Precoding: The Path to Privacy-Preserving Communications

no code implementations18 Sep 2021 Zhongxiang Wei, Christos Masouros, H. Vincent Poor, Athina P. Petropulu, Lajos Hanzo

In contrast to traditional security and privacy designs that aim to prevent confidential information from being eavesdropped upon by adversaries, or learned by unauthorized parties, in this paper we consider designs that mask the users' identities during communication, hence resulting in anonymous communications.

Joint LED Selection and Precoding Optimization for Multiple-User Multiple-Cell VLC Systems

no code implementations29 Aug 2021 Yang Yang, Yujie Yang, Mingzhe Chen, Chunyan Feng, Hailun Xia, Shuguang Cui, H. Vincent Poor

First, a MU-MC-VLC system model is established, and then a sum-rate maximization problem under dimming level and illumination uniformity constraints is formulated.

Federated Distributionally Robust Optimization for Phase Configuration of RISs

no code implementations20 Aug 2021 Chaouki Ben Issaid, Sumudu Samarakoon, Mehdi Bennis, H. Vincent Poor

In this article, we study the problem of robust reconfigurable intelligent surface (RIS)-aided downlink communication over heterogeneous RIS types in the supervised learning setting.

Rate-Splitting Multiple Access for Downlink MIMO: A Generalized Power Iteration Approach

no code implementations16 Aug 2021 Jeonghun Park, Jinseok Choi, Namyoon Lee, Wonjae Shin, H. Vincent Poor

Finding a linear precoding strategy that maximizes the sum spectral efficiency of RSMA is a challenging yet significant problem.

MetaSketch: Wireless Semantic Segmentation by Metamaterial Surfaces

no code implementations14 Aug 2021 Jingzhi Hu, Hongliang Zhang, Kaigui Bian, Zhu Han, H. Vincent Poor, Lingyang Song

Semantic segmentation is a process of partitioning an image into multiple segments for recognizing humans and objects, which can be widely applied in scenarios such as healthcare and safety monitoring.

Compressive Sensing Object Recognition +1

Simultaneously Transmitting and Reflecting (STAR) Intelligent Omni-Surfaces, Their Modeling and Implementation

no code implementations13 Aug 2021 Jiaqi Xu, Yuanwei Liu, Xidong Mu, Joey Tianyi Zhou, Lingyang Song, H. Vincent Poor, Lajos Hanzo

With the rapid development of advanced electromagnetic manipulation technologies, researchers and engineers are starting to study smart surfaces that can achieve enhanced coverages, high reconfigurability, and are easy to deploy.

6G Internet of Things: A Comprehensive Survey

no code implementations11 Aug 2021 Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, Dusit Niyato, Octavia Dobre, H. Vincent Poor

The sixth generation (6G) wireless communication networks are envisioned to revolutionize customer services and applications via the Internet of Things (IoT) towards a future of fully intelligent and autonomous systems.

Autonomous Driving

RIS-assisted UAV Communications for IoT with Wireless Power Transfer Using Deep Reinforcement Learning

no code implementations5 Aug 2021 Khoi Khac Nguyen, Antonino Masaracchia, Tan Do-Duy, H. Vincent Poor, Trung Q. Duong

We formulate a Markov decision process and propose two deep reinforcement learning algorithms to solve the optimization problem of maximizing the total network sum-rate.

Quality of Service Guarantees for Physical Unclonable Functions

no code implementations12 Jul 2021 Onur Günlü, Rafael F. Schaefer, H. Vincent Poor

A public ring oscillator (RO) output dataset is used to illustrate that a truncated Gaussian distribution can be fitted to transformed RO outputs that are inputs to uniform scalar quantizers such that reliability guarantees can be provided for each bit extracted from any PUF device under additive Gaussian noise components by eliminating a small subset of PUF outputs.

Federated Learning with Downlink Device Selection

no code implementations7 Jul 2021 Mohammad Mohammadi Amiri, Sanjeev R. Kulkarni, H. Vincent Poor

At each iteration, the PS broadcasts different quantized global model updates to different participating devices based on the last global model estimates available at the devices.

Federated Learning Image Classification

Low-Latency Federated Learning over Wireless Channels with Differential Privacy

no code implementations20 Jun 2021 Kang Wei, Jun Li, Chuan Ma, Ming Ding, Cailian Chen, Shi Jin, Zhu Han, H. Vincent Poor

Then, we convert the MAMAB to a max-min bipartite matching problem at each communication round, by estimating rewards with the upper confidence bound (UCB) approach.

Federated Learning

Decision-making Oriented Clustering: Application to Pricing and Power Consumption Scheduling

no code implementations2 Jun 2021 Chao Zhang, Samson Lasaulce, Martin Hennebel, Lucas Saludjian, Patrick Panciatici, H. Vincent Poor

For this purpose, we formulate the framework of decision-making oriented clustering and propose an algorithm providing a decision-based partition of the data space and good representative decisions.

Decision Making

Federated Learning for Industrial Internet of Things in Future Industries

no code implementations31 May 2021 Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, Dusit Niyato, H. Vincent Poor

The Industrial Internet of Things (IIoT) offers promising opportunities to transform the operation of industrial systems and becomes a key enabler for future industries.

Federated Learning

Intelligent Reflecting Surface-assisted Free-space Optical Communications

no code implementations13 May 2021 Vahid Jamali, Hedieh Ajam, Marzieh Najafi, Bernhard Schmauss, Robert Schober, H. Vincent Poor

Free-space optical (FSO) systems are able to offer the high data-rate, secure, and cost-efficient communication links required for applications such as wireless front- and backhauling for 5G and 6G communication networks.

Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design

1 code implementation10 May 2021 Chuan Ma, Jun Li, Ming Ding, Kang Wei, Wen Chen, H. Vincent Poor

Owing to the low communication costs and privacy-promoting capabilities, Federated Learning (FL) has become a promising tool for training effective machine learning models among distributed clients.

Federated Learning

Present and Future of Reconfigurable Intelligent Surface-Empowered Communications

no code implementations3 May 2021 Ertugrul Basar, H. Vincent Poor

Signal processing and communication communities have witnessed the rise of many exciting communication technologies in recent years.

Federated Learning for Internet of Things: A Comprehensive Survey

no code implementations16 Apr 2021 Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, H. Vincent Poor

The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of intelligent services and applications empowered by artificial intelligence (AI).

Federated Learning

Minimax Estimation of Linear Functions of Eigenvectors in the Face of Small Eigen-Gaps

no code implementations7 Apr 2021 Gen Li, Changxiao Cai, Yuantao Gu, H. Vincent Poor, Yuxin Chen

Eigenvector perturbation analysis plays a vital role in various statistical data science applications.


A General Derivative Identity for the Conditional Mean Estimator in Gaussian Noise and Some Applications

no code implementations5 Apr 2021 Alex Dytso, H. Vincent Poor, Shlomo Shamai

In the second part of the paper, via various choices of ${\bf U}$, the new identity is used to generalize many of the known identities and derive some new ones.

Distributed Reinforcement Learning for Age of Information Minimization in Real-Time IoT Systems

no code implementations4 Apr 2021 Sihua Wang, Mingzhe Chen, Zhaohui Yang, Changchuan Yin, Walid Saad, Shuguang Cui, H. Vincent Poor

In this paper, the problem of minimizing the weighted sum of age of information (AoI) and total energy consumption of Internet of Things (IoT) devices is studied.

Federated Learning: A Signal Processing Perspective

no code implementations31 Mar 2021 Tomer Gafni, Nir Shlezinger, Kobi Cohen, Yonina C. Eldar, H. Vincent Poor

Learning in a federated manner differs from conventional centralized machine learning, and poses several core unique challenges and requirements, which are closely related to classical problems studied in the areas of signal processing and communications.

Federated Learning

Spatio-temporal Modeling for Large-scale Vehicular Networks Using Graph Convolutional Networks

no code implementations13 Mar 2021 Juntong Liu, Yong Xiao, Yingyu Li, Guangming Shiyz, Walid Saad, H. Vincent Poor

The effective deployment of connected vehicular networks is contingent upon maintaining a desired performance across spatial and temporal domains.

Graph Convolutional Network Graph Reconstruction

Spatial Equalization Before Reception: Reconfigurable Intelligent Surfaces for Multi-path Mitigation

no code implementations8 Mar 2021 Hongliang Zhang, Lingyang Song, Zhu Han, H. Vincent Poor

Reconfigurable intelligent surfaces (RISs), which enable tunable anomalous reflection, have appeared as a promising method to enhance wireless systems.

Information Theory Information Theory

Optimization of User Selection and Bandwidth Allocation for Federated Learning in VLC/RF Systems

no code implementations5 Mar 2021 Chuanhong Liu, Caili Guo, Yang Yang, Mingzhe Chen, H. Vincent Poor, Shuguang Cui

Then, the problem of user selection and bandwidth allocation is studied for FL implemented over a hybrid VLC/RF system aiming to optimize the FL performance.

Federated Learning

MUSBO: Model-based Uncertainty Regularized and Sample Efficient Batch Optimization for Deployment Constrained Reinforcement Learning

no code implementations23 Feb 2021 DiJia Su, Jason D. Lee, John M. Mulvey, H. Vincent Poor

We consider a setting that lies between pure offline reinforcement learning (RL) and pure online RL called deployment constrained RL in which the number of policy deployments for data sampling is limited.

On the Application of BAC-NOMA to 6G umMTC

no code implementations12 Feb 2021 Zhiguo Ding, H. Vincent Poor

This letter studies the application of backscatter communications (BackCom) assisted non-orthogonal multiple access (BAC-NOMA) to the envisioned sixth-generation (6G) ultra-massive machine type communications (umMTC).

Information Theory Information Theory

Meta-Reinforcement Learning for Reliable Communication in THz/VLC Wireless VR Networks

1 code implementation29 Jan 2021 Yining Wang, Mingzhe Chen, Zhaohui Yang, Walid Saad, Tao Luo, Shuguang Cui, H. Vincent Poor

To control the energy consumption of the studied THz/VLC wireless VR network, VLC access points (VAPs) must be selectively turned on so as to ensure accurate and extensive positioning for VR users.

Meta Reinforcement Learning

Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimization

no code implementations28 Jan 2021 Kang Wei, Jun Li, Ming Ding, Chuan Ma, Yo-Seb Jeon, H. Vincent Poor

An attacker in FL may control a number of participant clients, and purposely craft the uploaded model parameters to manipulate system outputs, namely, model poisoning (MP).

Federated Learning

Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation

no code implementations18 Jan 2021 Jun Li, Yumeng Shao, Kang Wei, Ming Ding, Chuan Ma, Long Shi, Zhu Han, H. Vincent Poor

Focusing on this problem, we explore the impact of lazy clients on the learning performance of BLADE-FL, and characterize the relationship among the optimal K, the learning parameters, and the proportion of lazy clients.

Federated Learning

Context-Aware Security for 6G Wireless The Role of Physical Layer Security

no code implementations5 Jan 2021 Arsenia Chorti, Andre Noll Barreto, Stefan Kopsell, Marco Zoli, Marwa Chafii, Philippe Sehier, Gerhard Fettweis, H. Vincent Poor

Sixth generation systems are expected to face new security challenges, while opening up new frontiers towards context awareness in the wireless edge.

Cryptography and Security Signal Processing

Data-Driven Random Access Optimization in Multi-Cell IoT Networks with NOMA

no code implementations2 Jan 2021 Sami Khairy, Prasanna Balaprakash, Lin X. Cai, H. Vincent Poor

To enable a capacity-optimal network, a novel formulation of random channel access management is proposed, in which the transmission probability of each IoT device is tuned to maximize the geometric mean of users' expected capacity.

A Meta-Learning Approach to the Optimal Power Flow Problem Under Topology Reconfigurations

no code implementations21 Dec 2020 Yexiang Chen, Subhash Lakshminarayana, Carsten Maple, H. Vincent Poor

To overcome this drawback, we propose a DNN-based OPF predictor that is trained using a meta-learning (MTL) approach.


6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities

no code implementations14 Dec 2020 Md. Noor-A-Rahim, Zilong Liu, Haeyoung Lee, M. Omar Khyam, Jianhua He, Dirk Pesch, Klaus Moessner, Walid Saad, H. Vincent Poor

Aiming for truly intelligent transportation systems, we envision that machine learning will play an instrumental role for advanced vehicular communication and networking.

Autonomous Vehicles Information Theory Networking and Internet Architecture Information Theory

Robotic Communications for 5G and Beyond: Challenges and Research Opportunities

no code implementations9 Dec 2020 Yuanwei Liu, Xiao Liu, Xinyu Gao, Xidong Mu, Xiangwei Zhou, Octavia A. Dobre, H. Vincent Poor

Furthermore, dynamic trajectory design and resource allocation for both indoor and outdoor robots are provided to verify the performance of robotic communications in the context of typical robotic application scenarios.

Robotics Systems and Control Signal Processing Systems and Control

Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks

no code implementations6 Dec 2020 Ye Hu, Mingzhe Chen, Walid Saad, H. Vincent Poor, Shuguang Cui

Analytical results show that, the proposed VD-RL algorithm is guaranteed to converge to a local optimal solution of the non-convex optimization problem.


Blockchain Assisted Decentralized Federated Learning (BLADE-FL) with Lazy Clients

no code implementations2 Dec 2020 Jun Li, Yumeng Shao, Ming Ding, Chuan Ma, Kang Wei, Zhu Han, H. Vincent Poor

The proposed BLADE-FL has a good performance in terms of privacy preservation, tamper resistance, and effective cooperation of learning.

Federated Learning

Optimizing Resource-Efficiency for Federated Edge Intelligence in IoT Networks

no code implementations25 Nov 2020 Yong Xiao, Yingyu Li, Guangming Shi, H. Vincent Poor

The data uploading performance of IoT network and the computational capacity of edge servers are entangled with each other in influencing the FL model training process.

Federated Learning

Intelligent Omni-Surface: Ubiquitous Wireless Transmission by Reflective-Transmissive Metasurface

no code implementations2 Nov 2020 Shuhang Zhang, Hongliang Zhang, Boya Di, Yunhua Tan, Marco Di Renzo, Zhu Han, H. Vincent Poor, Lingyang Song

Intelligent reflecting surface (IRS), which is capable to adjust propagation conditions by controlling phase shifts of the reflected waves that impinge on the surface, has been widely analyzed for enhancing the performance of wireless systems.

Blind Federated Edge Learning

no code implementations19 Oct 2020 Mohammad Mohammadi Amiri, Tolga M. Duman, Deniz Gunduz, Sanjeev R. Kulkarni, H. Vincent Poor

At each iteration, wireless devices perform local updates using their local data and the most recent global model received from the PS, and send their local updates to the PS over a wireless fading multiple access channel (MAC).

Coded Stochastic ADMM for Decentralized Consensus Optimization with Edge Computing

no code implementations2 Oct 2020 Hao Chen, Yu Ye, Ming Xiao, Mikael Skoglund, H. Vincent Poor

A class of mini-batch stochastic alternating direction method of multipliers (ADMM) algorithms is explored to develop the distributed learning model.


Towards Self-learning Edge Intelligence in 6G

no code implementations1 Oct 2020 Yong Xiao, Guangming Shi, Yingyu Li, Walid Saad, H. Vincent Poor

Edge intelligence, also called edge-native artificial intelligence (AI), is an emerging technological framework focusing on seamless integration of AI, communication networks, and mobile edge computing.


Enhanced First and Zeroth Order Variance Reduced Algorithms for Min-Max Optimization

no code implementations28 Sep 2020 Tengyu Xu, Zhe Wang, Yingbin Liang, H. Vincent Poor

Specifically, a novel variance reduction algorithm SREDA was proposed recently by (Luo et al. 2020) to solve such a problem, and was shown to achieve the optimal complexity dependence on the required accuracy level $\epsilon$.

Learning Mixtures of Low-Rank Models

no code implementations23 Sep 2020 Yanxi Chen, Cong Ma, H. Vincent Poor, Yuxin Chen

We study the problem of learning mixtures of low-rank models, i. e. reconstructing multiple low-rank matrices from unlabelled linear measurements of each.

When Federated Learning Meets Blockchain: A New Distributed Learning Paradigm

no code implementations20 Sep 2020 Chuan Ma, Jun Li, Ming Ding, Long Shi, Taotao Wang, Zhu Han, H. Vincent Poor

Motivated by the explosive computing capabilities at end user equipments, as well as the growing privacy concerns over sharing sensitive raw data, a new machine learning paradigm, named federated learning (FL) has emerged.

Networking and Internet Architecture

A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G: Integrating Domain Knowledge into Deep Learning

no code implementations13 Sep 2020 Changyang She, Chengjian Sun, Zhouyou Gu, Yonghui Li, Chenyang Yang, H. Vincent Poor, Branka Vucetic

As one of the key communication scenarios in the 5th and also the 6th generation (6G) of mobile communication networks, ultra-reliable and low-latency communications (URLLC) will be central for the development of various emerging mission-critical applications.

Decision Making Decision Making Under Uncertainty

Convergence of Federated Learning over a Noisy Downlink

no code implementations25 Aug 2020 Mohammad Mohammadi Amiri, Deniz Gunduz, Sanjeev R. Kulkarni, H. Vincent Poor

The PS has access to the global model and shares it with the devices for local training, and the devices return the result of their local updates to the PS to update the global model.

Federated Learning Quantization

Cooperative Internet of UAVs: Distributed Trajectory Design by Multi-agent Deep Reinforcement Learning

no code implementations28 Jul 2020 Jingzhi Hu, Hongliang Zhang, Lingyang Song, Robert Schober, H. Vincent Poor

In this paper, we consider a cellular Internet of UAVs, where the UAVs execute sensing tasks through cooperative sensing and transmission to minimize the age of information (AoI).

Fast-Convergent Federated Learning

no code implementations26 Jul 2020 Hung T. Nguyen, Vikash Sehwag, Seyyedali Hosseinalipour, Christopher G. Brinton, Mung Chiang, H. Vincent Poor

In this paper, we propose a fast-convergent federated learning algorithm, called FOLB, which performs intelligent sampling of devices in each round of model training to optimize the expected convergence speed.

Federated Learning

Learning Centric Power Allocation for Edge Intelligence

no code implementations21 Jul 2020 Shuai Wang, Rui Wang, Qi Hao, Yik-Chung Wu, H. Vincent Poor

While machine-type communication (MTC) devices generate massive data, they often cannot process this data due to limited energy and computation power.


A Machine Learning Approach for Task and Resource Allocation in Mobile Edge Computing Based Networks

no code implementations20 Jul 2020 Sihua Wang, Mingzhe Chen, Xuanlin Liu, Changchuan Yin, Shuguang Cui, H. Vincent Poor

Since the data size of each computational task is different, as the requested computational task varies, the BSs must adjust their resource (subcarrier and transmit power) and task allocation schemes to effectively serve the users.

Edge-computing Q-Learning

A Novel Spectrally-Efficient Uplink Hybrid-Domain NOMA System

no code implementations17 Jul 2020 Chen Quan, Animesh Yadav, Baocheng Geng, Pramod K. Varshney, H. Vincent Poor

This paper proposes a novel hybrid-domain (HD) non-orthogonal multiple access (NOMA) approach to support a larger number of uplink users than the recently proposed code-domain NOMA approach, i. e., sparse code multiple access (SCMA).

Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization

no code implementations NeurIPS 2020 Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, H. Vincent Poor

In federated optimization, heterogeneity in the clients' local datasets and computation speeds results in large variations in the number of local updates performed by each client in each communication round.

Deep Learning for Wireless Communications: An Emerging Interdisciplinary Paradigm

no code implementations12 Jul 2020 Linglong Dai, Ruicheng Jiao, Fumiyuki Adachi, H. Vincent Poor, Lajos Hanzo

Hence, in this review, a pair of dominant methodologies of using DL for wireless communications are investigated.

Delay Minimization for Federated Learning Over Wireless Communication Networks

no code implementations5 Jul 2020 Zhaohui Yang, Mingzhe Chen, Walid Saad, Choong Seon Hong, Mohammad Shikh-Bahaei, H. Vincent Poor, Shuguang Cui

In this paper, the problem of delay minimization for federated learning (FL) over wireless communication networks is investigated.

Federated Learning

RDP-GAN: A Rényi-Differential Privacy based Generative Adversarial Network

2 code implementations4 Jul 2020 Chuan Ma, Jun Li, Ming Ding, Bo Liu, Kang Wei, Jian Weng, H. Vincent Poor

Generative adversarial network (GAN) has attracted increasing attention recently owing to its impressive ability to generate realistic samples with high privacy protection.

Decentralized Beamforming Design for Intelligent Reflecting Surface-enhanced Cell-free Networks

no code implementations22 Jun 2020 Shaocheng Huang, Yu Ye, Ming Xiao, H. Vincent Poor, Mikael Skoglund

Cell-free networks are considered as a promising distributed network architecture to satisfy the increasing number of users and high rate expectations in beyond-5G systems.

Federated Learning With Quantized Global Model Updates

no code implementations18 Jun 2020 Mohammad Mohammadi Amiri, Deniz Gunduz, Sanjeev R. Kulkarni, H. Vincent Poor

We analyze the convergence behavior of the proposed LFL algorithm assuming the availability of accurate local model updates at the server.

Federated Learning Quantization

Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters

no code implementations NeurIPS 2020 Kaiyi Ji, Jason D. Lee, Yingbin Liang, H. Vincent Poor

Although model-agnostic meta-learning (MAML) is a very successful algorithm in meta-learning practice, it can have high computational cost because it updates all model parameters over both the inner loop of task-specific adaptation and the outer-loop of meta initialization training.


Gradient Free Minimax Optimization: Variance Reduction and Faster Convergence

no code implementations16 Jun 2020 Tengyu Xu, Zhe Wang, Yingbin Liang, H. Vincent Poor

In this paper, we focus on such a gradient-free setting, and consider the nonconvex-strongly-concave minimax stochastic optimization problem.

Stochastic Optimization

Uncertainty quantification for nonconvex tensor completion: Confidence intervals, heteroscedasticity and optimality

no code implementations ICML 2020 Changxiao Cai, H. Vincent Poor, Yuxin Chen

Furthermore, our findings unveil the statistical optimality of nonconvex tensor completion: it attains un-improvable $\ell_{2}$ accuracy -- including both the rates and the pre-constants -- when estimating both the unknown tensor and the underlying tensor factors.

UVeQFed: Universal Vector Quantization for Federated Learning

1 code implementation5 Jun 2020 Nir Shlezinger, Mingzhe Chen, Yonina C. Eldar, H. Vincent Poor, Shuguang Cui

We show that combining universal vector quantization methods with FL yields a decentralized training system in which the compression of the trained models induces only a minimum distortion.

Federated Learning Quantization

Wireless Communications for Collaborative Federated Learning

no code implementations3 Jun 2020 Mingzhe Chen, H. Vincent Poor, Walid Saad, Shuguang Cui

However, due to resource constraints and privacy challenges, edge IoT devices may not be able to transmit their collected data to a central controller for training machine learning models.

Federated Learning

Meta-Reinforcement Learning for Trajectory Design in Wireless UAV Networks

no code implementations25 May 2020 Ye Hu, Mingzhe Chen, Walid Saad, H. Vincent Poor, Shuguang Cui

Meanwhile, the probability that the DBS serves over 50% of user requests increases about 27%, compared to the baseline policy gradient algorithm.

Meta-Learning Meta Reinforcement Learning

Nonparametric Estimation of the Fisher Information and Its Applications

no code implementations7 May 2020 Wei Cao, Alex Dytso, Michael Fauß, H. Vincent Poor, Gang Feng

First, an estimator proposed by Bhattacharya is revisited and improved convergence rates are derived.

Information-Theoretic Bounds on the Generalization Error and Privacy Leakage in Federated Learning

no code implementations5 May 2020 Semih Yagli, Alex Dytso, H. Vincent Poor

Second is the distributed setting in which each device trains its own model and send its model parameters to a central server where these model parameters are aggregated to create one final model.

Federated Learning

Downlink and Uplink Intelligent Reflecting Surface Aided Networks: NOMA and OMA

no code implementations3 May 2020 Yanyu Cheng, Kwok Hung Li, Yuanwei Liu, Kah Chan Teh, H. Vincent Poor

Intelligent reflecting surfaces (IRSs) are envisioned to provide reconfigurable wireless environments for future communication networks.

Energy-Efficient Wireless Communications with Distributed Reconfigurable Intelligent Surfaces

no code implementations1 May 2020 Zhaohui Yang, Mingzhe Chen, Walid Saad, Wei Xu, Mohammad Shikh-Bahaei, H. Vincent Poor, Shuguang Cui

In this network, multiple RISs are spatially distributed to serve wireless users and the energy efficiency of the network is maximized by dynamically controlling the on-off status of each RIS as well as optimizing the reflection coefficients matrix of the RISs.

Distributed Gradient Methods for Nonconvex Optimization: Local and Global Convergence Guarantees

no code implementations23 Mar 2020 Brian Swenson, Soummya Kar, H. Vincent Poor, José M. F. Moura, Aaron Jaech

We discuss local minima convergence guarantees and explore the simple but critical role of the stable-manifold theorem in analyzing saddle-point avoidance.

Optimization and Control

Federated Learning for Task and Resource Allocation in Wireless High Altitude Balloon Networks

no code implementations19 Mar 2020 Sihua Wang, Mingzhe Chen, Changchuan Yin, Walid Saad, Choong Seon Hong, Shuguang Cui, H. Vincent Poor

This problem is posed as an optimization problem whose goal is to minimize the energy and time consumption for task computing and transmission by adjusting the user association, service sequence, and task allocation scheme.

Edge-computing Federated Learning

The Vector Poisson Channel: On the Linearity of the Conditional Mean Estimator

no code implementations19 Mar 2020 Alex Dytso, Michael Fauss, H. Vincent Poor

The first result shows that the only distribution that induces the linearity of the conditional mean estimator is a product gamma distribution.

Malicious Experts versus the multiplicative weights algorithm in online prediction

no code implementations18 Mar 2020 Erhan Bayraktar, H. Vincent Poor, Xin Zhang

We assume that one of the experts is honest and makes correct prediction with probability $\mu$ at each round.

A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systems

no code implementations18 Mar 2020 Yo-Seb Jeon, Mohammad Mohammadi Amiri, Jun Li, H. Vincent Poor

One major challenge in system design is to reconstruct local gradient vectors accurately at the central server, which are computed-and-sent from the wireless devices.

Compressive Sensing Federated Learning

Distributed Stochastic Gradient Descent and Convergence to Local Minima

no code implementations5 Mar 2020 Brian Swenson, Ryan Murray, Soummya Kar, H. Vincent Poor

In centralized settings, it is well known that stochastic gradient descent (SGD) avoids saddle points.

Optimization and Control

User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization

no code implementations29 Feb 2020 Kang Wei, Jun Li, Ming Ding, Chuan Ma, Hang Su, Bo Zhang, H. Vincent Poor

According to our analysis, the UDP framework can realize $(\epsilon_{i}, \delta_{i})$-LDP for the $i$-th MT with adjustable privacy protection levels by varying the variances of the artificial noise processes.

Federated Learning

Reconfigurable Intelligent Surface Assisted MIMO Symbiotic Radio Networks

no code implementations2 Feb 2020 Qianqian Zhang, Ying-Chang Liang, H. Vincent Poor

In this paper, a novel reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) symbiotic radio (SR) system is proposed, in which an RIS, operating as a secondary transmitter (STx), sends messages to a multi-antenna secondary receiver (SRx) by using cognitive backscattering communication, and simultaneously, it enhances the primary transmission from a multi-antenna primary transmitter (PTx) to a multi-antenna primary receiver (PRx) by intelligently reconfiguring the wireless environment.

RIS Enhanced Massive Non-orthogonal Multiple Access Networks: Deployment and Passive Beamforming Design

no code implementations28 Jan 2020 Xiao Liu, Yuanwei Liu, Yue Chen, H. Vincent Poor

A novel framework is proposed for the deployment and passive beamforming design of a reconfigurable intelligent surface (RIS) with the aid of non-orthogonal multiple access (NOMA) technology.

Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge

no code implementations28 Jan 2020 Mohammad Mohammadi Amiri, Deniz Gunduz, Sanjeev R. Kulkarni, H. Vincent Poor

At each iteration of FL, a subset of the devices are scheduled to transmit their local model updates to the PS over orthogonal channel resources, while each participating device must compress its model update to accommodate to its link capacity.

Federated Learning

Convergence Time Optimization for Federated Learning over Wireless Networks

no code implementations22 Jan 2020 Mingzhe Chen, H. Vincent Poor, Walid Saad, Shuguang Cui

Due to the limited number of resource blocks (RBs) in a wireless network, only a subset of users can be selected to transmit their local FL model parameters to the BS at each learning step.

Federated Learning

Toward Optimal Adversarial Policies in the Multiplicative Learning System with a Malicious Expert

no code implementations2 Jan 2020 S. Rasoul Etesami, Negar Kiyavash, Vincent Leon, H. Vincent Poor

We consider a learning system based on the conventional multiplicative weight (MW) rule that combines experts' advice to predict a sequence of true outcomes.

Machine Intelligence at the Edge with Learning Centric Power Allocation

no code implementations12 Nov 2019 Shuai Wang, Yik-Chung Wu, Minghua Xia, Rui Wang, H. Vincent Poor

However, power allocation in this paradigm requires maximizing the learning performance instead of the communication throughput, for which the celebrated water-filling and max-min fairness algorithms become inefficient.

Fairness Learning Theory

Nonconvex Low-Rank Tensor Completion from Noisy Data

no code implementations NeurIPS 2019 Changxiao Cai, Gen Li, H. Vincent Poor, Yuxin Chen

We study a noisy tensor completion problem of broad practical interest, namely, the reconstruction of a low-rank tensor from highly incomplete and randomly corrupted observations of its entries.

Experienced Deep Reinforcement Learning with Generative Adversarial Networks (GANs) for Model-Free Ultra Reliable Low Latency Communication

no code implementations1 Nov 2019 Ali Taleb Zadeh Kasgari, Walid Saad, Mohammad Mozaffari, H. Vincent Poor

Formally, the URLLC resource allocation problem is posed as a power minimization problem under reliability, latency, and rate constraints.

Federated Learning with Differential Privacy: Algorithms and Performance Analysis

no code implementations1 Nov 2019 Kang Wei, Jun Li, Ming Ding, Chuan Ma, Howard H. Yang, Farokhi Farhad, Shi Jin, Tony Q. S. Quek, H. Vincent Poor

Specifically, the theoretical bound reveals the following three key properties: 1) There is a tradeoff between the convergence performance and privacy protection levels, i. e., a better convergence performance leads to a lower protection level; 2) Given a fixed privacy protection level, increasing the number $N$ of overall clients participating in FL can improve the convergence performance; 3) There is an optimal number of maximum aggregation times (communication rounds) in terms of convergence performance for a given protection level.

Federated Learning

Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networks

no code implementations31 Oct 2019 Howard H. Yang, Ahmed Arafa, Tony Q. S. Quek, H. Vincent Poor

Federated learning (FL) is a machine learning model that preserves data privacy in the training process.

Information Theory Signal Processing Information Theory

Subspace Estimation from Unbalanced and Incomplete Data Matrices: $\ell_{2,\infty}$ Statistical Guarantees

no code implementations9 Oct 2019 Changxiao Cai, Gen Li, Yuejie Chi, H. Vincent Poor, Yuxin Chen

This paper is concerned with estimating the column space of an unknown low-rank matrix $\boldsymbol{A}^{\star}\in\mathbb{R}^{d_{1}\times d_{2}}$, given noisy and partial observations of its entries.

A Joint Learning and Communications Framework for Federated Learning over Wireless Networks

1 code implementation17 Sep 2019 Mingzhe Chen, Zhaohui Yang, Walid Saad, Changchuan Yin, H. Vincent Poor, Shuguang Cui

This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm.

Federated Learning

On Safeguarding Privacy and Security in the Framework of Federated Learning

no code implementations14 Sep 2019 Chuan Ma, Jun Li, Ming Ding, Howard Hao Yang, Feng Shu, Tony Q. S. Quek, H. Vincent Poor

Motivated by the advancing computational capacity of wireless end-user equipment (UE), as well as the increasing concerns about sharing private data, a new machine learning (ML) paradigm has emerged, namely federated learning (FL).

Networking and Internet Architecture

Scheduling Policies for Federated Learning in Wireless Networks

no code implementations17 Aug 2019 Howard H. Yang, Zuozhu Liu, Tony Q. S. Quek, H. Vincent Poor

Due to limited bandwidth, only a portion of UEs can be scheduled for updates at each iteration.

Information Theory Signal Processing Information Theory

Robust Data Detection for MIMO Systems with One-Bit ADCs: A Reinforcement Learning Approach

no code implementations29 Mar 2019 Yo-Seb Jeon, Namyoon Lee, H. Vincent Poor

The key idea is to exploit input-output samples obtained from data detection, to compensate the mismatch in the likelihood function.

The Capacity Achieving Distribution for the Amplitude Constrained Additive Gaussian Channel: An Upper Bound on the Number of Mass Points

no code implementations10 Jan 2019 Alex Dytso, Semih Yagli, H. Vincent Poor, Shlomo Shamai

Finally, the third part provides bounds on the number of points for the case of $n=1$ with an additional power constraint.

Information Theory Information Theory

Partial Recovery of Erdős-Rényi Graph Alignment via $k$-Core Alignment

no code implementations10 Sep 2018 Daniel Cullina, Negar Kiyavash, Prateek Mittal, H. Vincent Poor

This estimator searches for an alignment in which the intersection of the correlated graphs using this alignment has a minimum degree of $k$.

On the Capacity of the Peak Power Constrained Vector Gaussian Channel: An Estimation Theoretic Perspective

1 code implementation23 Apr 2018 Alex Dytso, H. Vincent Poor, Shlomo Shamai

This paper characterizes the necessary and sufficient conditions on the constraint $R$ such that the input distribution supported on a single sphere is optimal.

Information Theory Information Theory

A Differential Privacy Mechanism Design Under Matrix-Valued Query

1 code implementation26 Feb 2018 Thee Chanyaswad, Alex Dytso, H. Vincent Poor, Prateek Mittal

noise to each element of the matrix, this method is often sub-optimal as it forfeits an opportunity to exploit the structural characteristics typically associated with matrix analysis.

Reinforcement Learning-based Energy Trading for Microgrids

no code implementations19 Jan 2018 Liang Xiao, Xingyu Xiao, Canhuang Dai, Mugen Pengy, Li-Chun Wang, H. Vincent Poor

The Nash quilibrium (NE) of the game is provided, revealing the conditions under which the local energy generation satisfies the energy demand of the MG and providing the performance bound of the energy trading scheme.

Systems and Control

MVG Mechanism: Differential Privacy under Matrix-Valued Query

no code implementations2 Jan 2018 Thee Chanyaswad, Alex Dytso, H. Vincent Poor, Prateek Mittal

To address this challenge, we propose a novel differential privacy mechanism called the Matrix-Variate Gaussian (MVG) mechanism, which adds a matrix-valued noise drawn from a matrix-variate Gaussian distribution, and we rigorously prove that the MVG mechanism preserves $(\epsilon,\delta)$-differential privacy.

Two-dimensional Anti-jamming Mobile Communication Based on Reinforcement Learning

no code implementations19 Dec 2017 Liang Xiao, Guoan Han, Donghua Jiang, Hongzi Zhu, Yanyong Zhang, H. Vincent Poor

It is shown that, by applying reinforcement learning techniques, a mobile device can achieve an optimal communication policy without the need to know the jamming and interference model and the radio channel model in a dynamic game framework.

A Learning-to-Infer Method for Real-Time Power Grid Multi-Line Outage Identification

no code implementations21 Oct 2017 Yue Zhao, Jianshu Chen, H. Vincent Poor

Identifying a potentially large number of simultaneous line outages in power transmission networks in real time is a computationally hard problem.

Stochastic Games for Smart Grid Energy Management with Prospect Prosumers

no code implementations6 Oct 2016 Seyed Rasoul Etesami, Walid Saad, Narayan Mandayam, H. Vincent Poor

For this case, it is shown that such an optimization problem admits a no-regret algorithm meaning that regardless of the actual outcome of the game among the prosumers, the utility company can follow a strategy that mitigates its allocation costs as if it knew the entire demand market a priori.

Nonparametric Detection of Geometric Structures over Networks

no code implementations5 Apr 2016 Shaofeng Zou, Yingbin Liang, H. Vincent Poor

Sufficient conditions on minimum and maximum sizes of candidate anomalous intervals are characterized in order to guarantee the proposed test to be consistent.

Machine Learning Methods for Attack Detection in the Smart Grid

no code implementations22 Mar 2015 Mete Ozay, Inaki Esnaola, Fatos T. Yarman Vural, Sanjeev R. Kulkarni, H. Vincent Poor

The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods.

Fusion of Image Segmentation Algorithms using Consensus Clustering

no code implementations18 Feb 2015 Mete Ozay, Fatos T. Yarman Vural, Sanjeev R. Kulkarni, H. Vincent Poor

A new segmentation fusion method is proposed that ensembles the output of several segmentation algorithms applied on a remotely sensed image.

Semantic Segmentation Stochastic Optimization

Nonparametric Detection of Anomalous Data Streams

no code implementations25 Apr 2014 Shaofeng Zou, Yingbin Liang, H. Vincent Poor, Xinghua Shi

samples drawn from a distribution p, whereas each anomalous sequence contains m i. i. d.

Two-sample testing

A Kernel-Based Nonparametric Test for Anomaly Detection over Line Networks

no code implementations1 Apr 2014 Shaofeng Zou, Yingbin Liang, H. Vincent Poor

If anomalous interval does not exist, then all nodes receive samples generated by p. It is assumed that the distributions p and q are arbitrary, and are unknown.

Anomaly Detection

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