Towards Private and Secure Federated Learning: Exploring the Interplay of Differential Privacy and Byzantine Robustness
Abstract
Federated Learning is a part of Machine Learning where the learning process occurs on edge devices instead of a centralized approach. With this shift towards decentralized data collection, the risk of privacy leaks and malicious attacks from participants has become prevalent and requires attention. While Federated Learning alone has shown the potential to address privacy issues, it remains an insufficient method to rely solely on for explicit security guarantees. Federated Learning has been proven to leak information from participating clients from their updates. In addition, because data is decentralized, there is little control over what each participant sends, creating a vulnerability attackers could exploit to send corrupted updates and prevent learning. To address these limitations, researchers have adopted standard methods such as Differential Privacy and Byzantine robustness. Differential Privacy has gained popularity as a procedure to ensure the privacy of Machine Learning, specifically Federated Learning models. Participants inject noise into their data and then send it to the server. These clients could exhibit Byzantine behavior and act maliciously to disrupt the final model; therefore, Byzantine robustness has been used to mitigate their influence. These notions of Differential Privacy and Byzantine robustness have been used to enforce privacy and security assurances. Both objectives need to be met to ensure that Federated Learning is both practical and…
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