Privacy-Preserving, Dropout-Resilient Aggregation in Decentralized Learning

arXiv:2404.17984v1 Announce Type: new
Abstract: Decentralized learning (DL) offers a novel paradigm in machine learning by distributing training across clients without central aggregation, enhancing scalability and efficiency. However, DL's peer-to-peer model raises challenges in protecting against inference attacks and privacy leaks. By forgoing central bottlenecks, DL demands privacy-preserving aggregation methods to protect data from 'honest but curious' clients and adversaries, maintaining network-wide privacy. Privacy-preserving DL faces the additional hurdle of client dropout, clients not submitting updates due to connectivity problems …

aggregation arxiv attacks challenges clients cs.ai cs.cr data decentralized demands efficiency leaks machine machine learning novel paradigm peer-to-peer privacy protect protect data protecting resilient scalability training