Latency-Oriented Secure Wireless Federated Learning: A Channel-Sharing Approach With Artificial Jamming
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2023.3234422
Abstract: As a promising framework for distributed machine learning (ML), wireless federated learning (FL) faces the threat of eavesdropping attacks when a trained ML model is sent over a radio channel. To address this threat, we… read more here.
Keywords: channel sharing; wireless federated; federated learning; approach artificial ... See more keywords
Wireless Federated Learning (WFL) for 6G Networks—Part II: The Compute-Then-Transmit NOMA Paradigm
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Communications Letters"
DOI: 10.1109/lcomm.2021.3121067
Abstract: As it has been discussed in the first part of this work, the utilization of advanced multiple access protocols and the joint optimization of the communication and computing resources can facilitate the reduction of delay… read more here.
Keywords: wfl; noma; wireless federated; transmit ... See more keywords
A Stackelberg Incentive Mechanism for Wireless Federated Learning With Differential Privacy
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Wireless Communications Letters"
DOI: 10.1109/lwc.2022.3181509
Abstract: In recent years, data privacy and security have attracted increasing attention in the age of artificial intelligence. Although federated learning (FL) can avoid data leakage by only sharing the machine learning models, it still suffers… read more here.
Keywords: incentive mechanism; wireless federated; federated learning; privacy ... See more keywords
Wireless Federated Learning With Dynamic Quantization and Bandwidth Adaptation
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Wireless Communications Letters"
DOI: 10.1109/lwc.2022.3202645
Abstract: Quantization has been increasingly proposed in wireless federated learning (FL) to compress transmission data, which therefore significantly reduces the training latency. In this letter, we further reduce the training latency by introducing dynamic quantization and… read more here.
Keywords: dynamic quantization; wireless federated; federated learning; quantization ... See more keywords