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Found 21 papers, 1 papers with code
Secure Aggregation is Not Private Against Membership Inference Attacks
In this paper, we delve into the privacy implications of SecAgg by treating it as a local differential privacy (LDP) mechanism for each local update.
FedStruct: Federated Decoupled Learning over Interconnected Graphs
We address the challenge of federated learning on graph-structured data distributed across multiple clients.
Time vs. Frequency Domain DPD for Massive MIMO: Methods and Performance Analysis
The use of up to hundreds of antennas in massive multi-user (MU) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) poses a complexity challenge for digital predistortion (DPD) aiming to linearize the nonlinear power amplifiers (PAs).
Blind Frequency-Domain Equalization Using Vector-Quantized Variational Autoencoders
We propose a novel frequency-domain blind equalization scheme for coherent optical communications.
Impact of Phase Noise on Uplink Cell-Free Massive MIMO OFDM
Cell-Free massive MIMO networks provide huge power gains and resolve inter-cell interference by coherent processing over a massive number of distributed instead of co-located antennas in access points (APs).
FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation
Inspired by group testing, the framework leverages overlapping groups of clients to identify the presence of malicious clients in the groups via a decoding operation.
Blind Channel Equalization Using Vector-Quantized Variational Autoencoders
Furthermore, we show that for the linear channel, the proposed scheme exhibits better convergence properties than the \ac{MMSE}-based, the \ac{CMA}-based, and the \ac{VAE}-based equalizers in terms of both convergence speed and robustness to variations in training batch size and learning rate.
Rateless Autoencoder Codes: Trading off Decoding Delay and Reliability
Most of today's communication systems are designed to target reliable message recovery after receiving the entire encoded message (codeword).
Frequency-domain digital predistortion for Massive MU-MIMO-OFDM Downlink
In this paper, we propose a convolutional neural network (CNN)-based DPD in the frequency domain, taking place before the precoding, where the dimensionality of the signal space depends on the number of users, instead of the number of BS antennas.
CodedPaddedFL and CodedSecAgg: Straggler Mitigation and Secure Aggregation in Federated Learning
For a scenario with 120 devices, CodedPaddedFL achieves a speed-up factor of 18 for an accuracy of 95% on the MNIST dataset compared to conventional FL.
Model-Based End-to-End Learning for WDM Systems With Transceiver Hardware Impairments
Simulation results show that the reinforcement-learning-based algorithm achieves similar performance to the standard supervised end-to-end learning approach assuming perfect channel knowledge.
Symbol-Based Over-the-Air Digital Predistortion Using Reinforcement Learning
We propose an over-the-air digital predistortion optimization algorithm using reinforcement learning.
Coding for Straggler Mitigation in Federated Learning
The proposed scheme combines one-time padding to preserve privacy and gradient codes to yield resiliency against stragglers and consists of two phases.
Over-the-fiber Digital Predistortion Using Reinforcement Learning
We demonstrate, for the first time, experimental over-the-fiber training of transmitter neural networks (NNs) using reinforcement learning.
Low Complexity Joint Impairment Mitigation of I/Q Modulator and PA Using Neural Networks
Neural networks (NNs) for multiple hardware impairments mitigation of a realistic direct conversion transmitter are impractical due to high computational complexity.
End-to-end Autoencoder for Superchannel Transceivers with Hardware Impairment
We propose an end-to-end learning-based approach for superchannel systems impaired by non-ideal hardware component.
Analysis and Design of Partially Information- and Partially Parity-Coupled Turbo Codes
In this paper, we study a class of spatially coupled turbo codes, namely partially information- and partially parity-coupled turbo codes.
Information Theory Signal Processing Information Theory
Residual Neural Networks for Digital Predistortion
Tracking the nonlinear behavior of an RF power amplifier (PA) is challenging.
Pruning Neural Belief Propagation Decoders
In this paper, we introduce a method to tailor an overcomplete parity-check matrix to (neural) BP decoding using machine learning.
A Droplet Approach Based on Raptor Codes for Distributed Computing With Straggling Servers
We propose a coded distributed computing scheme based on Raptor codes to address the straggler problem.
Block-Diagonal and LT Codes for Distributed Computing With Straggling Servers
We propose two coded schemes for the distributed computing problem of multiplying a matrix by a set of vectors.
