Security-Enhanced and Privacy-Preserving Federated Learning
Abstract
This thesis addresses two critical challenges in Federated Learning (FL): enhancing Byzantine robustness to thwart poisoning attacks and advancing privacy-preserving mechanisms to mitigate data leakage risks. To address the first challenge, we develop a robust FL aggregation scheme based on subjective logic and residual-based attack detection. This scheme retains historical data from previous rounds to build client reputations. Employing a combination of theoretical analysis, trace-driven simulation, and experimental validation with a prototype and real users, we show that our FL classifier can detect sensitive content with high accuracy, learn new labels rapidly, and remain robust against poisoning attacks from malicious users as well as imperfect input from non-malicious ones. Additionally, we identify a major weakness in FL—its vulnerability to poisoning attacks—as a consequence of the one person one vote (henceforth 1p1v) principle underlying most contemporary aggregation rules. To overcome this, we introduce FedQV, a novel aggregation algorithm built upon the quadratic voting scheme, recently proposed as a better alternative to 1p1v-based elections. Our theoretical analysis establishes that FedQV is a truthful mechanism in which bidding according to one’s true valuation is a dominant strategy that achieves a convergence rate matching that of state-of-the-art methods. Furthermore, our empirical analysis using multiple real-world datasets validates the superior…
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