Sanitizer: Blazing-Fast, Private, and Robust Federated Learning

Recently, private and robust federated learning (FL) schemes have been proposed to address privacy inference and Byzantine attacks simultaneously. However, existing schemes are inefficient in private and robust aggregation protocols due to the employment of heavy cryptographic techniques. To approach the above problem, we propose Sanitizer, an efficient, private, and robust FL framework. Specifically, we first design a Byzantine-robust defense for communication-efficient sign-based FL. We further propose a customized private and robust aggregation scheme built on our Byzantine-robust defense for FL. The core of our construction is two new efficient protocols, i.e.,high-dimensional boolean summationandweighted boolean majority vote, which serve as the main building blocks of Sanitizer. Extensive evaluations on real-world datasets demonstrate that Sanitizer is blazing fast, achieving 19 ∼ 23× less runtime compared to the state-of-the-art. Meanwhile, Sanitizer achieves the same accuracy as the plaintext and superior Byzantine robustness against various classic attacks.