ScionFL: Efficient and Robust Secure Quantized Aggregation

no code implementations • 13 Oct 2022 • Yaniv Ben-Itzhak, Helen Möllering, Benny Pinkas, Thomas Schneider, Ajith Suresh, Oleksandr Tkachenko, Shay Vargaftik, Christian Weinert, Hossein Yalame, Avishay Yanai

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.

Federated Learning Quantization

Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications

no code implementations • 10 Jan 2018 • M. Sadegh Riazi, Christian Weinert, Oleksandr Tkachenko, Ebrahim. M. Songhori, Thomas Schneider, Farinaz Koushanfar

Chameleon departs from the common assumption of additive or linear secret sharing models where three or more parties need to communicate in the online phase: the framework allows two parties with private inputs to communicate in the online phase under the assumption of a third node generating correlated randomness in an offline phase.

BIG-bench Machine Learning