Byzantine-Robust Aggregation for Securing Decentralized Federated Learning
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Access"
DOI: 10.1109/access.2025.3629864
Abstract: Federated Learning (FL) emerges as a distributed machine learning approach that addresses privacy concerns by training AI models locally on devices. Decentralized Federated Learning (DFL) extends the FL paradigm by eliminating the central server, thereby… read more here.
Keywords: robust aggregation; byzantine; federated learning; byzantine robust ... See more keywords
PBFL: Privacy-Preserving and Byzantine-Robust Federated-Learning-Empowered Industry 4.0
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2023.3315226
Abstract: In Industry 4.0, artificial intelligence (AI) has been successfully applied in scenarios, such as fault prediction, traffic analysis, and production decision making. However, due to the sensitivity and security of data, privacy regulations prohibit the… read more here.
Keywords: privacy preserving; industry; federated learning; preserving byzantine ... See more keywords
GeoFed: Geometry-Aware Byzantine Robust Federated Learning on SPD Manifolds in Heterogeneous Environments
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2025.3584818
Abstract: Federated learning (FL) has been increasingly applied in the Internet of Things (IoT), leveraging its decentralized nature to facilitate collaboration among clients and enable resource-constrained clients to jointly train a globally optimal model based on… read more here.
Keywords: byzantine robust; geofed geometry; model; byzantine ... See more keywords
Communication-Efficient and Byzantine-Robust Differentially Private Federated Learning
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Communications Letters"
DOI: 10.1109/lcomm.2022.3180113
Abstract: Federated learning, as a novel paradigm of machine learning, is facing a series of challenges such as efficiency, privacy and robustness. The recently proposed EF-DP- SIGNSGD provides theoretical privacy protection for SIGNSGD with majority vote… read more here.
Keywords: efficient byzantine; communication efficient; federated learning; byzantine ... See more keywords
Byzantine-Robust Distributed Stochastic Nonconvex Optimization in Adversarial Environments Over Unbalanced Networks
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Automatic Control"
DOI: 10.1109/tac.2025.3579218
Abstract: In this article, we focused on a Byzantine-robust distributed stochastic nonconvex optimization problem with smooth local cost functions over unbalanced networks. In particular, the nodes in a network are to find a stationary solution minimizing… read more here.
Keywords: unbalanced networks; robust distributed; byzantine robust; distributed stochastic ... See more keywords
Privacy-Preserving Byzantine-Robust Federated Learning via Blockchain Systems
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Information Forensics and Security"
DOI: 10.1109/tifs.2022.3196274
Abstract: Federated learning enables clients to train a machine learning model jointly without sharing their local data. However, due to the centrality of federated learning framework and the untrustworthiness of clients, traditional federated learning solutions are… read more here.
Keywords: blockchain; preserving byzantine; federated learning; privacy preserving ... See more keywords