FedClean: A Defense Mechanism Against Parameter Poisoning Attacks in Federated Learning
Abhishek Kumar, Vivek Khimani, Dimitris Chatzopoulos, Pan Hui
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12 May 2022
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.
