Search Results for author: Huaiyu Dai

Found 35 papers, 5 papers with code

Distributed Learning over Networks with Graph-Attention-Based Personalization

no code implementations22 May 2023 Zhuojun Tian, Zhaoyang Zhang, Zhaohui Yang, Richeng Jin, Huaiyu Dai

In conventional distributed learning over a network, multiple agents collaboratively build a common machine learning model.

Graph Attention

On the $f$-Differential Privacy Guarantees of Discrete-Valued Mechanisms

no code implementations19 Feb 2023 Richeng Jin, Zhonggen Su, Caijun Zhong, Zhaoyang Zhang, Tony Quek, Huaiyu Dai

We consider a federated data analytics problem in which a server coordinates the collaborative data analysis of multiple users with privacy concerns and limited communication capability.

Federated Learning

Graph Neural Networks Meet Wireless Communications: Motivation, Applications, and Future Directions

no code implementations8 Dec 2022 Mengyuan Lee, Guanding Yu, Huaiyu Dai, Geoffrey Ye Li

As an efficient graph analytical tool, graph neural networks (GNNs) have special properties that are particularly fit for the characteristics and requirements of wireless communications, exhibiting good potential for the advancement of next-generation wireless communications.

Multi-Job Intelligent Scheduling with Cross-Device Federated Learning

no code implementations24 Nov 2022 Ji Liu, Juncheng Jia, Beichen Ma, Chendi Zhou, Jingbo Zhou, Yang Zhou, Huaiyu Dai, Dejing Dou

The system model enables a parallel training process of multiple jobs, with a cost model based on the data fairness and the training time of diverse devices during the parallel training process.

Bayesian Optimization Fairness +2

Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehicles

no code implementations27 Oct 2022 Md Ferdous Pervej, Richeng Jin, Huaiyu Dai

This paper proposes a vehicular edge federated learning (VEFL) solution, where an edge server leverages highly mobile connected vehicles' (CVs') onboard central processing units (CPUs) and local datasets to train a global model.

Federated Learning

Privacy-Preserving Decentralized Inference with Graph Neural Networks in Wireless Networks

no code implementations15 Aug 2022 Mengyuan Lee, Guanding Yu, Huaiyu Dai

As an efficient neural network model for graph data, graph neural networks (GNNs) recently find successful applications for various wireless optimization problems.

Efficient Neural Network Management +1

Gradient Obfuscation Gives a False Sense of Security in Federated Learning

no code implementations8 Jun 2022 Kai Yue, Richeng Jin, Chau-Wai Wong, Dror Baron, Huaiyu Dai

Prior work has shown that the gradient sharing strategies in federated learning can be vulnerable to data reconstruction attacks.

Federated Learning Image Classification +2

Mobility, Communication and Computation Aware Federated Learning for Internet of Vehicles

no code implementations17 May 2022 Md Ferdous Pervej, Jianlin Guo, Kyeong Jin Kim, Kieran Parsons, Philip Orlik, Stefano Di Cairano, Marcel Menner, Karl Berntorp, Yukimasa Nagai, Huaiyu Dai

To take the high mobility of vehicles into account, we consider the delay as a learning parameter and restrict it to be less than a tolerable threshold.

Federated Learning

FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server

no code implementations25 Apr 2022 Hong Zhang, Ji Liu, Juncheng Jia, Yang Zhou, Huaiyu Dai, Dejing Dou

Despite achieving remarkable performance, Federated Learning (FL) suffers from two critical challenges, i. e., limited computational resources and low training efficiency.

Federated Learning

Neural Tangent Kernel Empowered Federated Learning

no code implementations7 Oct 2021 Kai Yue, Richeng Jin, Ryan Pilgrim, Chau-Wai Wong, Dror Baron, Huaiyu Dai

The paradigm addresses the challenge of statistical heterogeneity by transmitting update data that are more expressive than those of the conventional FL paradigms.

Federated Learning Privacy Preserving

Federated Learning via Plurality Vote

1 code implementation6 Oct 2021 Kai Yue, Richeng Jin, Chau-Wai Wong, Huaiyu Dai

Federated learning allows collaborative workers to solve a machine learning problem while preserving data privacy.

Federated Learning Quantization

On the exploitative behavior of adversarial training against adversarial attacks

no code implementations29 Sep 2021 Ali Rahmati, Seyed-Mohsen Moosavi-Dezfooli, Huaiyu Dai

Adversarial attacks have been developed as intentionally designed perturbations added to the inputs in order to fool deep neural network classifiers.

60 GHz Outdoor Propagation Measurements and Analysis Using Facebook Terragraph Radios

no code implementations2 Sep 2021 Kairui Du, Omkar Mujumdar, Ozgur Ozdemir, Ender Ozturk, Ismail Guvenc, Mihail L. Sichitiu, Huaiyu Dai, Arupjyoti Bhuyan

In this work, we investigated the propagation and scattering behavior of 60 GHz mmWave signals in outdoor environments at a travel distance of 98 m for an aerial link (rooftop to rooftop), and 147 m for a ground link (light-pole to light-pole).

Experimental Study of Outdoor UAV Localization and Tracking using Passive RF Sensing

no code implementations17 Aug 2021 Udita Bhattacherjee, Ender Ozturk, Ozgur Ozdemir, Ismail Guvenc, Mihail L. Sichitiu, Huaiyu Dai

First, the Keysight sensor detects the UAV by comparing the received RF signature with various other UAVs' RF signatures in the Keysight database using an envelope detection algorithm.

Channel Rank Improvement in Urban Drone Corridors Using Passive Intelligent Reflectors

no code implementations4 Aug 2021 Ender Ozturk, Chethan Kumar Anjinappa, Fatih Erden, Ismail Guvenc, Huaiyu Dai, Arupjyoti Bhuyan

Multiple-input multiple-output (MIMO) techniques can help in scaling the achievable air-to-ground (A2G) channel capacity while communicating with drones.

Communication-Efficient Federated Learning via Predictive Coding

1 code implementation2 Aug 2021 Kai Yue, Richeng Jin, Chau-Wai Wong, Huaiyu Dai

In each communication round, we select the predictor and quantizer based on the rate-distortion cost, and further reduce the redundancy with entropy coding.

Data Compression Federated Learning +1

Adversarial training may be a double-edged sword

no code implementations24 Jul 2021 Ali Rahmati, Seyed-Mohsen Moosavi-Dezfooli, Huaiyu Dai

Adversarial training has been shown as an effective approach to improve the robustness of image classifiers against white-box attacks.

Precoder Design for Physical-Layer Security and Authentication in Massive MIMO UAV Communications

no code implementations2 Jul 2021 Sung Joon Maeng, Yavuz Yapıcı, İsmail Güvenç, Arupjyoti Bhuyan, Huaiyu Dai

Supporting reliable and seamless wireless connectivity for unmanned aerial vehicles (UAVs) has recently become a critical requirement to enable various different use cases of UAVs.


Base Station Antenna Uptilt Optimization for Cellular-Connected Drone Corridors

no code implementations2 Jul 2021 Sung Joon Maeng, Md Moin Uddin Chowdhury, İsmail Güvenç, Arupjyoti Bhuyan, Huaiyu Dai

The concept of drone corridors is recently getting more attention to enable connected, safe, and secure flight zones in the national airspace.

Decentralized Inference with Graph Neural Networks in Wireless Communication Systems

no code implementations19 Apr 2021 Mengyuan Lee, Guanding Yu, Huaiyu Dai

Different from other neural network models, GNN can be implemented in a decentralized manner with information exchanges among neighbors, making it a potentially powerful tool for decentralized control in wireless communication systems.

Efficient Neural Network

Modeling the Nonsmoothness of Modern Neural Networks

no code implementations26 Mar 2021 Runze Liu, Chau-Wai Wong, Huaiyu Dai

Modern neural networks have been successful in many regression-based tasks such as face recognition, facial landmark detection, and image generation.

Face Recognition Facial Landmark Detection +2

Distributed ADMM with Synergetic Communication and Computation

no code implementations29 Sep 2020 Zhuojun Tian, Zhaoyang Zhang, Jue Wang, Xiaoming Chen, Wei Wang, Huaiyu Dai

In this paper, we propose a novel distributed alternating direction method of multipliers (ADMM) algorithm with synergetic communication and computation, called SCCD-ADMM, to reduce the total communication and computation cost of the system.

A Fast Graph Neural Network-Based Method for Winner Determination in Multi-Unit Combinatorial Auctions

no code implementations29 Sep 2020 Mengyuan Lee, Seyyedali Hosseinalipour, Christopher G. Brinton, Guanding Yu, Huaiyu Dai

However, the problem of allocating items among the bidders to maximize the auctioneers" revenue, i. e., the winner determination problem (WDP), is NP-complete to solve and inapproximable.

Multi-Stage Hybrid Federated Learning over Large-Scale D2D-Enabled Fog Networks

1 code implementation18 Jul 2020 Seyyedali Hosseinalipour, Sheikh Shams Azam, Christopher G. Brinton, Nicolo Michelusi, Vaneet Aggarwal, David J. Love, Huaiyu Dai

We derive the upper bound of convergence for MH-FL with respect to parameters of the network topology (e. g., the spectral radius) and the learning algorithm (e. g., the number of D2D rounds in different clusters).

Federated Learning

From Federated to Fog Learning: Distributed Machine Learning over Heterogeneous Wireless Networks

no code implementations7 Jun 2020 Seyyedali Hosseinalipour, Christopher G. Brinton, Vaneet Aggarwal, Huaiyu Dai, Mung Chiang

There are several challenges with employing conventional federated learning in contemporary networks, due to the significant heterogeneity in compute and communication capabilities that exist across devices.

BIG-bench Machine Learning Federated Learning +1

Lifetime Maximization for UAV-assisted Data Gathering Networks in the Presence of Jamming

no code implementations10 May 2020 Ali Rahmati, Seyyedali Hosseinalipour, Ismail Guvenc, Huaiyu Dai, Arupjyoti Bhuyan

Deployment of unmanned aerial vehicles (UAVs) is recently getting significant attention due to a variety of practical use cases, such as surveillance, data gathering, and commodity delivery.

Communication Efficient Federated Learning with Energy Awareness over Wireless Networks

no code implementations15 Apr 2020 Richeng Jin, Xiaofan He, Huaiyu Dai

Moreover, most of the existing works assume Channel State Information (CSI) available at both the mobile devices and the parameter server, and thus the mobile devices can adopt fixed transmission rates dictated by the channel capacity.

Federated Learning

GeoDA: a geometric framework for black-box adversarial attacks

1 code implementation CVPR 2020 Ali Rahmati, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard, Huaiyu Dai

We propose a geometric framework to generate adversarial examples in one of the most challenging black-box settings where the adversary can only generate a small number of queries, each of them returning the top-$1$ label of the classifier.

Accelerating Generalized Benders Decomposition for Wireless Resource Allocation

1 code implementation3 Mar 2020 Mengyuan Lee, Ning Ma, Guanding Yu, Huaiyu Dai

Only useful cuts are added to the master problem and thus the complexity of the master problem is reduced.

Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees

no code implementations25 Feb 2020 Richeng Jin, Yufan Huang, Xiaofan He, Huaiyu Dai, Tianfu Wu

We present Stochastic-Sign SGD which utilizes novel stochastic-sign based gradient compressors enabling the aforementioned properties in a unified framework.

Federated Learning Quantization

Distributed Byzantine Tolerant Stochastic Gradient Descent in the Era of Big Data

no code implementations27 Feb 2019 Richeng Jin, Xiaofan He, Huaiyu Dai

The recent advances in sensor technologies and smart devices enable the collaborative collection of a sheer volume of data from multiple information sources.

BIG-bench Machine Learning

Decentralized Differentially Private Without-Replacement Stochastic Gradient Descent

no code implementations8 Sep 2018 Richeng Jin, Xiaofan He, Huaiyu Dai

While machine learning has achieved remarkable results in a wide variety of domains, the training of models often requires large datasets that may need to be collected from different individuals.

BIG-bench Machine Learning

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