SpyShield: a Spyfall inspired defense mechanism against poisoning attacks in federated learning.

ABSTRACT: Traditional machine learning (ML) relies on a centralized architecture, which makes it unsuitable for applications where data privacy is critical. Federated Learning (FL) addresses this issue by allowing multiple parties to collaboratively train models without sharing their raw data. However, FL is susceptible to data and model poisoning attacks that can severely disrupt the learning process. Existing literature indicates that defense mechanisms predominantly analyze client updates on the server side, often without requiring or involving client cooperation. This paper proposes a novel defense mechanism, SpyShield, that leverages client cooperation to identify malicious clients in data and model poisoning attacks. SpyShield is inspired by tactics used in the social deduction game Spyfall, w

SUBMITTER: Elgharieb Y 

PROVIDER: S-EPMC12381036 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature