Topology-Based Reconstruction Prevention for Decentralised Learning

arXiv:2312.05248v2 Announce Type: replace
Abstract: Decentralised learning has recently gained traction as an alternative to federated learning in which both data and coordination are distributed over its users. To preserve data confidentiality, decentralised learning relies on differential privacy, multi-party computation, or a combination thereof. However, running multiple privacy-preserving summations in sequence may allow adversaries to perform reconstruction attacks. Unfortunately, current reconstruction countermeasures either cannot trivially be adapted to the distributed setting, or add excessive amounts of noise.
In this work, …

adversaries arxiv computation confidentiality coordination cs.cr cs.dc cs.dm cs.lg data differential privacy distributed federated federated learning may party prevention privacy running