Search Results for author:
Found 101 papers, 25 papers with code
GINO-Q: Learning an Asymptotically Optimal Index Policy for Restless Multi-armed Bandits
GINO-Q mitigates the curse of dimensionality by decomposing the RMAB into a series of subproblems, each with the same dimension as a single arm, ensuring that complexity increases linearly with the number of arms.
Private Collaborative Edge Inference via Over-the-Air Computation
We consider collaborative inference at the wireless edge, where each client's model is trained independently on their local datasets.
Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid Byzantines in Federated Learning
Hence, inspired by the sparse neural networks, we introduce a hybrid sparse Byzantine attack that is composed of two parts: one exhibiting a sparse nature and attacking only certain NN locations with higher sensitivity, and the other being more silent but accumulating over time, where each ideally targets a different type of defence mechanism, and together they form a strong but imperceptible attack.
Process-and-Forward: Deep Joint Source-Channel Coding Over Cooperative Relay Networks
We introduce deep joint source-channel coding (DeepJSCC) schemes for image transmission over cooperative relay channels.
Remote Estimation of Markov Processes over Costly Channels: On the Benefits of Implicit Information
We show that the main challenge in solving this problem is associated with the consideration of implicit information, i. e., information that the monitor can obtain about the source when the sensor is silent.
Friendly Attacks to Improve Channel Coding Reliability
We demonstrate that the proposed friendly attack method can improve the reliability across different channels, modulations, codes, and decoders.
Point Cloud in the Air
Acquisition and processing of point clouds (PCs) is a crucial enabler for many emerging applications reliant on 3D spatial data, such as robot navigation, autonomous vehicles, and augmented reality.
Adaptive Early Exiting for Collaborative Inference over Noisy Wireless Channels
In this work, we study early exiting in the context of collaborative inference, which allows obtaining inference results at the edge device for certain samples, without the need to transmit the partially processed data to the edge server at all, leading to further communication savings.
A Hybrid Joint Source-Channel Coding Scheme for Mobile Multi-hop Networks
To this end, we propose a hybrid solution, where DeepJSCC is adopted for the first hop, while the received signal at the first relay is digitally compressed and forwarded through the mobile core network.
Distributed Deep Joint Source-Channel Coding with Decoder-Only Side Information
We consider low-latency image transmission over a noisy wireless channel when correlated side information is present only at the receiver side (the Wyner-Ziv scenario).
Semi-Federated Learning: Convergence Analysis and Optimization of A Hybrid Learning Framework
To tackle this issue, we propose a semi-federated learning (SemiFL) paradigm to leverage the computing capabilities of both the BS and devices for a hybrid implementation of centralized learning (CL) and FL.
High Perceptual Quality Wireless Image Delivery with Denoising Diffusion Models
We consider the image transmission problem over a noisy wireless channel via deep learning-based joint source-channel coding (DeepJSCC) along with a denoising diffusion probabilistic model (DDPM) at the receiver.
Communication Efficient Private Federated Learning Using Dithering
The task of preserving privacy while ensuring efficient communication is a fundamental challenge in federated learning.
Adaptive Compression in Federated Learning via Side Information
The high communication cost of sending model updates from the clients to the server is a significant bottleneck for scalable federated learning (FL).
Data-Heterogeneous Hierarchical Federated Learning with Mobility
Federated learning enables distributed training of machine learning (ML) models across multiple devices in a privacy-preserving manner.
Wireless Point Cloud Transmission
3D point cloud is a three-dimensional data format generated by LiDARs and depth sensors, and is being increasingly used in a large variety of applications.
DeepJSCC-l++: Robust and Bandwidth-Adaptive Wireless Image Transmission
This paper presents a novel vision transformer (ViT) based deep joint source channel coding (DeepJSCC) scheme, dubbed DeepJSCC-l++, which can be adaptive to multiple target bandwidth ratios as well as different channel signal-to-noise ratios (SNRs) using a single model.
Semantic Communication of Learnable Concepts
The transmitter applies a learning algorithm to the available examples, and extracts knowledge from the data by optimizing a probability distribution over a set of models, i. e., known functions, which can better describe the observed data, and so potentially the underlying concepts.
Graph Neural Networks over the Air for Decentralized Tasks in Wireless Networks
Considering a GNN implemented over nodes connected through wireless links, this paper conducts a stability analysis to study the impact of channel impairments on the performance of GNNs, and proposes graph neural networks over the air (AirGNNs), a novel GNN architecture that incorporates the communication model.
Vicious Classifiers: Data Reconstruction Attack at Inference Time
Privacy-preserving inference in edge computing paradigms encourages the users of machine-learning services to locally run a model on their private input, for a target task, and only share the model's outputs with the server.
Generative Joint Source-Channel Coding for Semantic Image Transmission
In GenerativeJSCC, we carry out end-to-end training of an encoder and a StyleGAN-based decoder, and show that GenerativeJSCC significantly outperforms DeepJSCC both in terms of distortion and perceptual quality.
Intelligent Computing: The Latest Advances, Challenges and Future
In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications.
Distributed Deep Joint Source-Channel Coding over a Multiple Access Channel
We consider distributed image transmission over a noisy multiple access channel (MAC) using deep joint source-channel coding (DeepJSCC).
Feedback is Good, Active Feedback is Better: Block Attention Active Feedback Codes
Deep neural network (DNN)-assisted channel coding designs, such as low-complexity neural decoders for existing codes, or end-to-end neural-network-based auto-encoder designs are gaining interest recently due to their improved performance and flexibility; particularly for communication scenarios in which high-performing structured code designs do not exist.
Decentralized Channel Management in WLANs with Graph Neural Networks
Wireless local area networks (WLANs) manage multiple access points (APs) and assign scarce radio frequency resources to APs for satisfying traffic demands of associated user devices.
Space-time design for deep joint source channel coding of images Over MIMO channels
We propose novel deep joint source-channel coding (DeepJSCC) algorithms for wireless image transmission over multi-input multi-output (MIMO) Rayleigh fading channels, when channel state information (CSI) is available only at the receiver.
A Meta-Learning Based Gradient Descent Algorithm for MU-MIMO Beamforming
Multi-user multiple-input multiple-output (MU-MIMO) beamforming design is typically formulated as a non-convex weighted sum rate (WSR) maximization problem that is known to be NP-hard.
Sparse Random Networks for Communication-Efficient Federated Learning
At the end of the training, the final model is a sparse network with random weights -- or a subnetwork inside the dense random network.
Byzantines can also Learn from History: Fall of Centered Clipping in Federated Learning
The centered clipping (CC) framework has further shown that the momentum term from the previous iteration, besides reducing the variance, can be used as a reference point to neutralize Byzantine attacks better.
Semantic Communications with Discrete-time Analog Transmission: A PAPR Perspective
Recent progress in deep learning (DL)-based joint source-channel coding (DeepJSCC) has led to a new paradigm of semantic communications.
Privacy Against Inference Attacks in Vertical Federated Learning
In the prediction phase, with logistic regression as the classification model, several inference attack techniques are proposed that the adversary, i. e., the active party, can employ to reconstruct the passive party's features, regarded as sensitive information.
Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications
Communication systems to date primarily aim at reliably communicating bit sequences.
Neural Distributed Image Compression with Cross-Attention Feature Alignment
In the proposed method, the decoder employs a cross-attention module to align the feature maps obtained from the received latent representation of the input image and a latent representation of the side information.
Bottlenecks CLUB: Unifying Information-Theoretic Trade-offs Among Complexity, Leakage, and Utility
In this work, we propose a general family of optimization problems, termed as complexity-leakage-utility bottleneck (CLUB) model, which (i) provides a unified theoretical framework that generalizes most of the state-of-the-art literature for the information-theoretic privacy models, (ii) establishes a new interpretation of the popular generative and discriminative models, (iii) constructs new insights to the generative compression models, and (iv) can be used in the fair generative models.
Learning-based Autonomous Channel Access in the Presence of Hidden Terminals
We consider the problem of autonomous channel access (AutoCA), where a group of terminals tries to discover a communication strategy with an access point (AP) via a common wireless channel in a distributed fashion.
All you need is feedback: Communication with block attention feedback codes
Deep learning based channel code designs have recently gained interest as an alternative to conventional coding algorithms, particularly for channels for which existing codes do not provide effective solutions.
DeepJSCC-Q: Constellation Constrained Deep Joint Source-Channel Coding
Recent works have shown that modern machine learning techniques can provide an alternative approach to the long-standing joint source-channel coding (JSCC) problem.
AttentionCode: Ultra-Reliable Feedback Codes for Short-Packet Communications
The training methods can potentially be generalized to other wireless communication applications with machine learning.
Privacy Amplification via Random Participation in Federated Learning
In this work, in a federated setting, we consider random participation of the clients in addition to subsampling their local datasets.
Active Privacy-Utility Trade-off Against Inference in Time-Series Data Sharing
For privacy measure, we consider both the probability of correctly detecting the true value of the secret and the mutual information (MI) between the secret and the released data.
Remote Contextual Bandits
We consider a remote contextual multi-armed bandit (CMAB) problem, in which the decision-maker observes the context and the reward, but must communicate the actions to be taken by the agents over a rate-limited communication channel.
Over-the-Air Ensemble Inference with Model Privacy
We consider distributed inference at the wireless edge, where multiple clients with an ensemble of models, each trained independently on a local dataset, are queried in parallel to make an accurate decision on a new sample.
DRF Codes: Deep SNR-Robust Feedback Codes
We present a new deep-neural-network (DNN) based error correction code for fading channels with output feedback, called deep SNR-robust feedback (DRF) code.
Collaborative Learning over Wireless Networks: An Introductory Overview
This is partly due to the communication bottleneck limiting the overall computation speed.
Privacy-Aware Communication Over a Wiretap Channel with Generative Networks
We study privacy-aware communication over a wiretap channel using end-to-end learning.
Contextual Multi-Armed Bandit with Communication Constraints
We consider a remote Contextual Multi-Armed Bandit (CMAB) problem, in which the decision-maker observes the context and the reward, but must communicate the actions to be taken by the agents over a rate-limited communication channel.
Bayesian Over-The-Air Computation
In the low signal-to-noise ratio (SNR) regime, the LMMSE estimator reduces the mean squared error (MSE) by at least 6 dB; in the high SNR regime, the LMMSE estimator lowers the error floor of MSE by 86. 4%; 2) For the asynchronous OAC, our LMMSE and sum-product maximum a posteriori (SP-MAP) estimators are on an equal footing in terms of the MSE performance, and are significantly better than the ML estimator.
Learning to Minimize Age of Information over an Unreliable Channel with Energy Harvesting
The time average expected age of information (AoI) is studied for status updates sent over an error-prone channel from an energy-harvesting transmitter with a finite-capacity battery.
Neural Distributed Image Compression using Common Information
The received latent representation and the locally generated common information are passed through a decoder network to obtain an enhanced reconstruction of the input image.
Less is More: Feature Selection for Adversarial Robustness with Compressive Counter-Adversarial Attacks
Hence, we propose a novel approach to identify the important features by employing counter-adversarial attacks, which highlights the consistency at the penultimate layer with respect to perturbations on input samples.
AirNet: Neural Network Transmission over the Air
We further improve the performance of AirNet by pruning the network below the available bandwidth, and expanding it for improved robustness.
Denoising Noisy Neural Networks: A Bayesian Approach with Compensation
Deep neural networks (DNNs) with noisy weights, which we refer to as noisy neural networks (NoisyNNs), arise from the training and inference of DNNs in the presence of noise.
Deep Extended Feedback Codes
A new deep-neural-network (DNN) based error correction encoder architecture for channels with feedback, called Deep Extended Feedback (DEF), is presented in this paper.
LIDAR and Position-Aided mmWave Beam Selection with Non-local CNNs and Curriculum Training
Efficient millimeter wave (mmWave) beam selection in vehicle-to-infrastructure (V2I) communication is a crucial yet challenging task due to the narrow mmWave beamwidth and high user mobility.
Gradient Coding with Dynamic Clustering for Straggler-Tolerant Distributed Learning
Distributed implementations are crucial in speeding up large scale machine learning applications.
Federated Edge Learning with Misaligned Over-The-Air Computation
Over-the-air computation (OAC) is a promising technique to realize fast model aggregation in the uplink of federated edge learning.
A Reinforcement Learning Approach to Age of Information in Multi-User Networks with HARQ
Scheduling the transmission of time-sensitive information from a source node to multiple users over error-prone communication channels is studied with the goal of minimizing the long-term average age of information (AoI) at the users.
Active Privacy-utility Trade-off Against a Hypothesis Testing Adversary
We consider a user releasing her data containing some personal information in return of a service.
Speeding Up Private Distributed Matrix Multiplication via Bivariate Polynomial Codes
We consider the problem of private distributed matrix multiplication under limited resources.
Information Theory Cryptography and Security Distributed, Parallel, and Cluster Computing Information Theory
Federated mmWave Beam Selection Utilizing LIDAR Data
Efficient link configuration in millimeter wave (mmWave) communication systems is a crucial yet challenging task due to the overhead imposed by beam selection.
Time-Correlated Sparsification for Communication-Efficient Federated Learning
Sparse communication is often employed to reduce the communication load, where only a small subset of the model updates are communicated from the clients to the PS.
Effective Communications: A Joint Learning and Communication Framework for Multi-Agent Reinforcement Learning over Noisy Channels
Specifically, we consider a multi-agent partially observable Markov decision process (MA-POMDP), in which the agents, in addition to interacting with the environment can also communicate with each other over a noisy communication channel.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
FedADC: Accelerated Federated Learning with Drift Control
The core of the FL strategy is the use of stochastic gradient descent (SGD) in a distributed manner.
Private Wireless Federated Learning with Anonymous Over-the-Air Computation
In conventional federated learning (FL), differential privacy (DP) guarantees can be obtained by injecting additional noise to local model updates before transmitting to the parameter server (PS).
FedRec: Federated Learning of Universal Receivers over Fading Channels
Wireless communications is often subject to channel fading.
Distributed Sparse SGD with Majority Voting
However, top-K sparsification requires additional communication load to represent the sparsity pattern, and the mismatch between the sparsity patterns of the workers prevents exploitation of efficient communication protocols.
Gradient Coding with Dynamic Clustering for Straggler Mitigation
In distributed synchronous gradient descent (GD) the main performance bottleneck for the per-iteration completion time is the slowest \textit{straggling} workers.
Blind Federated Edge Learning
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).
Communicate to Learn at the Edge
Bringing the success of modern machine learning (ML) techniques to mobile devices can enable many new services and businesses, but also poses significant technical and research challenges.
Meta-learning based Alternating Minimization Algorithm for Non-convex Optimization
In this paper, we propose a novel solution for non-convex problems of multiple variables, especially for those typically solved by an alternating minimization (AM) strategy that splits the original optimization problem into a set of sub-problems corresponding to each variable, and then iteratively optimize each sub-problem using a fixed updating rule.
Convergence of Federated Learning over a Noisy Downlink
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.
Wireless Image Retrieval at the Edge
We propose two alternative schemes based on digital and analog communications, respectively.
Secure Distributed Matrix Computation with Discrete Fourier Transform
We consider the problem of secure distributed matrix computation (SDMC), where a \textit{user} queries a function of data matrices generated at distributed \textit{source} nodes.
Information Theory Cryptography and Security Distributed, Parallel, and Cluster Computing Information Theory
Coded Distributed Computing with Partial Recovery
In this paper, we first introduce a novel coded matrix-vector multiplication scheme, called coded computation with partial recovery (CCPR), which benefits from the advantages of both coded and uncoded computation schemes, and reduces both the computation time and the decoding complexity by allowing a trade-off between the accuracy and the speed of computation.
Pruning the Pilots: Deep Learning-Based Pilot Design and Channel Estimation for MIMO-OFDM Systems
Our pruning-based pilot reduction technique reduces the overhead by allocating pilots across subcarriers non-uniformly and exploiting the inter-frequency and inter-antenna correlations in the channel matrix efficiently through convolutional layers and attention module.
Federated Learning With Quantized Global Model Updates
We analyze the convergence behavior of the proposed LFL algorithm assuming the availability of accurate local model updates at the server.
Age-Based Coded Computation for Bias Reduction in Distributed Learning
To mitigate biased estimators, we design a $timely$ dynamic encoding framework for partial recovery that includes an ordering operator that changes the codewords and computation orders at workers over time.
Federated Learning in Vehicular Networks
Machine learning (ML) has recently been adopted in vehicular networks for applications such as autonomous driving, road safety prediction and vehicular object detection, due to its model-free characteristic, allowing adaptive fast response.
Straggler-aware Distributed Learning: Communication Computation Latency Trade-off
When gradient descent (GD) is scaled to many parallel workers for large scale machine learning problems, its per-iteration computation time is limited by the straggling workers.
Distributed Deep Convolutional Compression for Massive MIMO CSI Feedback
We also propose a distributed version of DeepCMC for a multi-user MIMO scenario to encode and reconstruct the CSI from multiple users in a distributed manner.
Decentralized SGD with Over-the-Air Computation
We study the performance of decentralized stochastic gradient descent (DSGD) in a wireless network, where the nodes collaboratively optimize an objective function using their local datasets.
Privacy-Aware Time-Series Data Sharing with Deep Reinforcement Learning
We measure the privacy leakage by the mutual information between the user's true data sequence and shared version.
Joint Device-Edge Inference over Wireless Links with Pruning
We propose a joint feature compression and transmission scheme for efficient inference at the wireless network edge.
Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge
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.
One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysis
We provide a comprehensive analysis of the effects of wireless channel hostilities (channel noise, fading, and channel estimation errors) on the convergence rate of the proposed FEEL scheme.
Information Theory Distributed, Parallel, and Cluster Computing Networking and Internet Architecture Signal Processing Information Theory
Deep Joint Source-Channel Coding for Wireless Image Retrieval
Motivated by surveillance applications with wireless cameras or drones, we consider the problem of image retrieval over a wireless channel.
CNN-based Analog CSI Feedback in FDD MIMO-OFDM Systems
Massive multiple-input multiple-output (MIMO) systems require downlink channel state information (CSI) at the base station (BS) to better utilize the available spatial diversity and multiplexing gains.
Hierarchical Federated Learning Across Heterogeneous Cellular Networks
We study collaborative machine learning (ML) across wireless devices, each with its own local dataset.
Federated Learning over Wireless Fading Channels
Overall these results show clear advantages for the proposed analog over-the-air DSGD scheme, which suggests that learning and communication algorithms should be designed jointly to achieve the best end-to-end performance in machine learning applications at the wireless edge.
Privacy-Aware Location Sharing with Deep Reinforcement Learning
Existing approaches are mainly focused on privacy of sharing a single location or myopic location trace privacy; neither of them taking into account the temporal correlations between the past and current locations.
Information Theory Cryptography and Security Information Theory
Collaborative Machine Learning at the Wireless Edge with Blind Transmitters
At each iteration of the DSGD algorithm wireless devices compute gradient estimates with their local datasets, and send them to the PS over a wireless fading multiple access channel (MAC).
Machine Learning in the Air
Thanks to the recent advances in processing speed and data acquisition and storage, machine learning (ML) is penetrating every facet of our lives, and transforming research in many areas in a fundamental manner.
Practical Functional Regenerating Codes for Broadcast Repair of Multiple Nodes
A code construction and repair scheme for optimal functional regeneration of multiple node failures is presented, which is based on stitching together short MDS codes on carefully chosen sets of points lying on a linearized polynomial.
Information Theory Information Theory
Successive Refinement of Images with Deep Joint Source-Channel Coding
We introduce deep learning based communication methods for successive refinement of images over wireless channels.
Gradient Coding with Clustering and Multi-message Communication
Gradient descent (GD) methods are commonly employed in machine learning problems to optimize the parameters of the model in an iterative fashion.
The Best Defense Is a Good Offense: Adversarial Attacks to Avoid Modulation Detection
We consider a communication scenario, in which an intruder tries to determine the modulation scheme of the intercepted signal.
Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air
Following this digital approach, we introduce D-DSGD, in which the wireless devices employ gradient quantization and error accumulation, and transmit their gradient estimates to the PS over a multiple access channel (MAC).
Distributed Gradient Descent with Coded Partial Gradient Computations
Coded computation techniques provide robustness against straggling servers in distributed computing, with the following limitations: First, they increase decoding complexity.
Deep Joint Source-Channel Coding for Wireless Image Transmission
We propose a joint source and channel coding (JSCC) technique for wireless image transmission that does not rely on explicit codes for either compression or error correction; instead, it directly maps the image pixel values to the complex-valued channel input symbols.
Speeding Up Distributed Gradient Descent by Utilizing Non-persistent Stragglers
In most of the existing DGD schemes, either with coded computation or coded communication, the non-straggling CSs transmit one message per iteration once they complete all their assigned computation tasks.
Multi-Access Communications with Energy Harvesting: A Multi-Armed Bandit Model and the Optimality of the Myopic Policy
The energy arrival process at each node is modelled as an independent two-state Markov process, such that, at each TS, a node either harvests one unit of energy, or none.
