Recently, decentralized learning has emerged as a popular peer-to-peer signal and
information processing paradigm that enables model training across geographically …

Mobile Crowdsensing leverages the widespread use of smartphones to gather valuable
data for various applications. It primarily involves users who either request or perform …

In this paper, we focus on robust sparse M-estimation over decentralized networks in the
presence of Byzantine attacks. In particular, a decentralized network is modeled as an …

We introduce a locally differentially private (LDP) algorithm for online federated learning that
employs temporally correlated noise to improve utility while preserving privacy. To address …

This paper considers the problem of decentralized resource allocation in the presence of
Byzantine attacks. Such attacks occur when an unknown number of malicious agents send …

Recently, decentralized learning has emerged as a popular peer-to-peer signal and
information processing paradigm that enables model training across geographically …

Privacy leakage and Byzantine failures are two adverse factors to the intelligent decision-
making process of multi-agent systems (MASs). Considering the presence of these two …

Decentralized stochastic gradient algorithms resolve efficiently large-scale finite-sum
optimization problems when all agents over networks are reliable. However, most of these …

This paper addresses the challenge of robust sparse M-estimation in decentralized networks
under Byzantine attacks. In a decentralized learning framework, some nodes may behave …