Privacy-Preserving Federated Learning With Backdoor Resilience
Abstract
Federated learning (FL) has the ability to train a global model across many different clients with diverse datasets while also preserving privacy. However, federated learning is by design vulnerable to privacy inference attacks and poisoning attacks, allowing compromised clients to infer private information about the clients or negatively influence the global model, respectively. FLAME [26], a state-of-the-art framework, is designed to statistically remove the influence of poisoning attacks, while being applicable to many attacker models and keeping the model’s benign performance. To ensure these objectives, the FLAME protocol introduces a defense framework that estimates the minimum sufficient amount of noise to be injected into the global model after aggregation, so that backdoors are eliminated but the benign performance does not deteriorate. To further reduce the amount of noise and enhance the desired goals, FLAME utilizes adaptive clustering and weight clipping. However, federated learning systems that implement FLAME still face significant privacy risks from inference attacks, where malicious aggregators can exploit access to model updates to extract sensitive information about client data. Therefore, it is imperative to also achieve malicious security. In this thesis, we propose an implementation of FLAME in combination with secure Multi- Party Computation (MPC), which provides the primitives to protect federated learning against inference attacks. We propose two…
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