FedClean: A Defense Mechanism against Parameter Poisoning Attacks in Federated Learning
Abhishek Kumar, Vivek Khimani, Dimitris Chatzopoulos, Pan Hui
2022ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)14 citationsDOIOpen Access PDF
Abstract
In Federated learning (FL) systems, a centralized entity (server), instead of access to the training data, has access to model parameter updates computed by each participant independently and based solely on their samples. Unfortunately, FL is susceptible to model poisoning attacks, in which malicious or malfunctioning entities share polluted updates that can compromise the model’s accuracy. In this study, we propose FedClean, an FL mechanism that is robust to model poisoning attacks. The accuracy of the models trained with the assistance of FedClean is close to the one where malicious entities do not participate.
Topics & Concepts
CompromiseFederated learningComputer scienceComputer securityMechanism (biology)Training setServerArtificial intelligenceComputer networkSocial scienceSociologyEpistemologyPhilosophyPrivacy-Preserving Technologies in DataAdversarial Robustness in Machine LearningCryptography and Data Security