ScionFL: Efficient and Robust Secure Quantized Aggregation

Yaniv Ben-Itzhak, Helen Möllering, Benny Pinkas, Thomas Schneider, Ajith Suresh, Oleksandr Tkachenko, shay vargaftik, Christian Weinert, Hossein Yalame, Avishay Yanai

Keywords: secure aggregation, federated learning, multi-party computation, robustness, quantization, communication-efficiency, privacy

TL;DR: We enable efficient and robust MPC-based secure aggregation based on quantized gradients.

Abstract:

Secure aggregation is commonly used in federated learning (FL) to alleviate privacy concerns related to the central aggregator seeing all parameter updates in the clear. Unfortunately, most existing secure aggregation schemes ignore two critical orthogonal research directions that aim to (i) significantly reduce client-server communication and (ii) mitigate the impact of malicious clients.
However, both of these additional properties are essential to facilitate cross-device FL with thousands or even millions of (mobile) participants.

In this paper, we unite both research directions by introducing ScionFL, the first secure aggregation framework for FL that operates efficiently on quantized inputs and simultaneously provides robustness against malicious clients. Our framework leverages novel multi-party computation MPC techniques and supports multiple linear (1-bit) quantization schemes, including ones that utilize the randomized Hadamard transform and Kashin's representation.

Our theoretical results are supported by extensive evaluations. We show that with no overhead for clients and moderate overhead for the server compared to transferring and processing quantized updates in plaintext, we obtain comparable accuracy for standard FL benchmarks. Moreover, we demonstrate the robustness of our framework against state-of-the-art poisoning attacks.

Submission Number: 113