A Lightweight Committee-Based Approach for Privacy-Preserving Federated Learning

Despite its advantages for privacy-preserving data-driven modeling, federated learning is vulnerable to privacy breaches, as demonstrated by recent attacks on its privacy properties. It has been proven that sharing the weights alone is insufficient to protect the underlying data. In this work, we provide a solution for sharing the aggregated weights with a central server while safeguarding the privacy of individual client weights. Our solution introduces a decentralized committee election mechanism, eliminating the need for a trusted party. The election phase is based on verifiable random functions (VRFs), whereas the aggregation phase is based on Elliptic curve cryptography and multi-party secret-sharing schemes. Our experimental results show that our solution outperforms the proposed solutions in terms of communication and computation costs. Overall, our approach offers a robust solution for privacy-preserving federated learning without compromising its accuracy and without relying on a third party.