OASIS: Offsetting Active Reconstruction Attacks in Federated Learning

arXiv:2311.13739v2 Announce Type: replace
Abstract: Federated Learning (FL) has garnered significant attention for its potential to protect user privacy while enhancing model training efficiency. For that reason, FL has found its use in various domains, from healthcare to industrial engineering, especially where data cannot be easily exchanged due to sensitive information or privacy laws. However, recent research has demonstrated that FL protocols can be easily compromised by active reconstruction attacks executed by dishonest servers. These attacks involve the malicious modification …

arxiv attacks attention cs.ai cs.cr data domains efficiency engineering federated federated learning found healthcare industrial information model training oasis privacy protect sensitive sensitive information training user privacy