Search Results for author:
Found 42 papers, 7 papers with code
Content-Level Selective Offloading in Heterogeneous Networks: Multi-armed Bandit Optimization and Regret Bounds
It is shown that the proposed algorithms learn the popularity profile quickly for a wide range of system parameters.
The Multi-layer Information Bottleneck Problem
Based on information forwarded by the preceding layer, each stage of the network is required to preserve a certain level of relevance with regards to a specific hidden variable, quantified by the mutual information.
A Reinforcement Learning Approach to Age of Information in Multi-User Networks
Scheduling the transmission of time-sensitive data 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 under a constraint on the average number of transmissions at the source node.
Deep Convolutional Compression for Massive MIMO CSI Feedback
In comparison with previous works, the main contributions of DeepCMC are two-fold: i) DeepCMC is fully convolutional, and it can be used in a wide range of scenarios with various numbers of sub-channels and transmit antennas; ii) DeepCMC includes quantization and entropy coding blocks and minimizes a cost function that accounts for both the rate of compression and the reconstruction quality of the channel matrix at the BS.
Energy-Aware Analog Aggregation for Federated Learning with Redundant Data
In this work, we consider analog aggregation to scale down the communication cost with respect to the number of workers, and introduce data redundancy to the system to deal with non-i. i. d.
DeepJSCC-f: Deep Joint Source-Channel Coding of Images with Feedback
It is well known that separation is not optimal in the practical finite blocklength regime; however, there are no known practical joint source-channel coding (JSCC) schemes that can exploit the feedback signal and surpass the performance of separation-based schemes.
Distributed Deep Reinforcement Learning for Functional Split Control in Energy Harvesting Virtualized Small Cells
We formulate the corresponding grid energy and traffic drop rate minimization problem, and propose a distributed deep reinforcement learning (DDRL) solution.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Bandwidth-Agile Image Transmission with Deep Joint Source-Channel Coding
We propose deep learning based communication methods for adaptive-bandwidth transmission of images over wireless channels.
Dopamine: Differentially Private Federated Learning on Medical Data
While rich medical datasets are hosted in hospitals distributed across the world, concerns on patients' privacy is a barrier against using such data to train deep neural networks (DNNs) for medical diagnostics.
Distributed Learning in Wireless Networks: Recent Progress and Future Challenges
Then, we present a detailed literature review on the use of communication techniques for its efficient deployment.
Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers' Outputs
It is known that deep neural networks, trained for the classification of non-sensitive target attributes, can reveal sensitive attributes of their input data through internal representations extracted by the classifier.
Quantifying and Localizing Usable Information Leakage from Neural Network Gradients
Our proposed framework enables clients to localize and quantify the private information leakage in a layer-wise manner, and enables a better understanding of the sources of information leakage in collaborative learning, which can be used by future studies to benchmark new attacks and defense mechanisms.
Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraints
In this work, we consider an over-the-air FEEL system with analog gradient aggregation, and propose an energy-aware dynamic device scheduling algorithm to optimize the training performance under energy constraints of devices, where both communication energy for gradient aggregation and computation energy for local training are included.
Variational Leakage: The Role of Information Complexity in Privacy Leakage
We study the role of information complexity in privacy leakage about an attribute of an adversary's interest, which is not known a priori to the system designer.
Fast Federated Edge Learning with Overlapped Communication and Computation and Channel-Aware Fair Client Scheduling
We consider federated edge learning (FEEL) over wireless fading channels taking into account the downlink and uplink channel latencies, and the random computation delays at the clients.
Coded Computation across Shared Heterogeneous Workers with Communication Delay
In this work, we consider a multi-master heterogeneous-worker distributed computing scenario, where multiple matrix multiplication tasks are encoded and allocated to workers for parallel computation.
DeepWiVe: Deep-Learning-Aided Wireless Video Transmission
We present DeepWiVe, the first-ever end-to-end joint source-channel coding (JSCC) video transmission scheme that leverages the power of deep neural networks (DNNs) to directly map video signals to channel symbols, combining video compression, channel coding, and modulation steps into a single neural transform.
DeepJSCC-Q: Channel Input Constrained Deep Joint Source-Channel Coding
Recent works have shown that the task of wireless transmission of images can be learned with the use of machine learning techniques.
Hierarchical Over-the-Air Federated Edge Learning
Federated learning (FL) over wireless communication channels, specifically, over-the-air (OTA) model aggregation framework is considered.
Cost-Efficient Distributed Learning via Combinatorial Multi-Armed Bandits
We consider the distributed SGD problem, where a main node distributes gradient calculations among $n$ workers.
Time-Correlated Sparsification for Efficient Over-the-Air Model Aggregation in Wireless Federated Learning
In this work, we propose time-correlated sparsification with hybrid aggregation (TCS-H) for communication-efficient FEEL, which exploits jointly the power of model compression and over-the-air computation.
Robust Federated Learning with Connectivity Failures: A Semi-Decentralized Framework with Collaborative Relaying
Intermittent connectivity of clients to the parameter server (PS) is a major bottleneck in federated edge learning frameworks.
Federated Spatial Reuse Optimization in Next-Generation Decentralized IEEE 802.11 WLANs
As wireless standards evolve, more complex functionalities are introduced to address the increasing requirements in terms of throughput, latency, security, and efficiency.
Rate-Constrained Remote Contextual Bandits
We consider a rate-constrained contextual multi-armed bandit (RC-CMAB) problem, in which a group of agents are solving the same contextual multi-armed bandit (CMAB) problem.
Transformer-Empowered 6G Intelligent Networks: From Massive MIMO Processing to Semantic Communication
It is anticipated that 6G wireless networks will accelerate the convergence of the physical and cyber worlds and enable a paradigm-shift in the way we deploy and exploit communication networks.
Semi-Decentralized Federated Learning with Collaborative Relaying
We present a semi-decentralized federated learning algorithm wherein clients collaborate by relaying their neighbors' local updates to a central parameter server (PS).
A Learning Aided Flexible Gradient Descent Approach to MISO Beamforming
This paper proposes a learning aided gradient descent (LAGD) algorithm to solve the weighted sum rate (WSR) maximization problem for multiple-input single-output (MISO) beamforming.
Over-the-Air Federated Edge Learning with Hierarchical Clustering
Our scheme utilizes OTA cluster aggregations for the communication of the MUs with their corresponding IS, and OTA global aggregations from the ISs to the PS.
MOB-FL: Mobility-Aware Federated Learning for Intelligent Connected Vehicles
Federated learning (FL) is a promising approach to enable the future Internet of vehicles consisting of intelligent connected vehicles (ICVs) with powerful sensing, computing and communication capabilities.
Device Selection for the Coexistence of URLLC and Distributed Learning Services
Our simulation results confirm that our solution can significantly decrease the training delay of the distributed AI service while keeping the URLLC availability above its required threshold and close to the scenario where URLLC solely consumes all network resources.
Collaborative Semantic Communication for Edge Inference
We study the collaborative image retrieval problem at the wireless edge, where multiple edge devices capture images of the same object from different angles and locations, which are then used jointly to retrieve similar images at the edge server over a shared multiple access channel (MAC).
Features-over-the-Air: Contrastive Learning Enabled Cooperative Edge Inference
We study the collaborative image retrieval problem at the wireless edge, where multiple edge devices capture images of the same object, which are then used jointly to retrieve similar images at the edge server over a shared multiple access channel.
Sustainable Edge Intelligence Through Energy-Aware Early Exiting
However, due to their computational complexity, DL models consume significant amounts of energy, which can rapidly drain the battery and compromise the performance of IoT devices.
Secure Deep-JSCC Against Multiple Eavesdroppers
Adversarial accuracy of eavesdroppers are also studied over Rayleigh fading, Nakagami-m, and AWGN channels to verify the generalization of the proposed scheme.
CommIN: Semantic Image Communications as an Inverse Problem with INN-Guided Diffusion Models
In this work, we propose CommIN, which views the recovery of high-quality source images from degraded reconstructions as an inverse problem.
Multi-Agent Reinforcement Learning for Power Control in Wireless Networks via Adaptive Graphs
The ever-increasing demand for high-quality and heterogeneous wireless communication services has driven extensive research on dynamic optimization strategies in wireless networks.
Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networks
Hierarchical federated learning (HFL) enables distributed training of models across multiple devices with the help of several edge servers and a cloud edge server in a privacy-preserving manner.
Maximal-Capacity Discrete Memoryless Channel Identification
Based on this capacity estimator, a gap-elimination algorithm termed BestChanID is proposed, which is oblivious to the capacity-achieving input distribution and is guaranteed to output the DMC with the largest capacity, with a desired confidence.
Goal-Oriented and Semantic Communication in 6G AI-Native Networks: The 6G-GOALS Approach
Recent advances in AI technologies have notably expanded device intelligence, fostering federation and cooperation among distributed AI agents.
Extreme Video Compression with Pre-trained Diffusion Models
The results showcase the potential of exploiting the temporal relations in video data using generative models.
Evolving Semantic Communication with Generative Model
In this paper, we explore an evolving semantic communication system for image transmission, referred to as ESemCom, with the capability to continuously enhance transmission efficiency.
The Rate-Distortion-Perception Trade-off: The Role of Private Randomness
The per-symbol near-perfect realism constraint requires that the TVD between the distribution of output symbol $Y_t$ and the source distribution be arbitrarily small, uniformly in the index $t.$ We characterize the corresponding asymptotic rate-distortion trade-off and show that encoder private randomness is not useful if the compression rate is lower than the entropy of the source, however limited the resources in terms of common randomness and decoder private randomness may be.
