Federated Learning trains machine learning models on distributed devices by aggregating local model updates instead of local data. However, privacy concerns arise as the aggregated local models on the server may reveal sensitive personal information by inversion attacks. Privacy-preserving methods, such as homomorphic encryption (HE), then become necessary for FL training. Despite HE's privacy advantages, its applications suffer from impractical overheads, especially for foundation models. In this paper, we present FedML-HE, the first practical federated learning system with efficient HE-based secure model aggregation. FedML-HE proposes to selectively encrypt sensitive parameters, significantly reducing both computation and communication overheads during training while providing customizable privacy preservation. Our optimized system demonstrates considerable overhead reduction, particularly for large foundation models (e.g., ~10x reduction for ResNet-50, and up to ~40x reduction for BERT), demonstrating the potential for scalable HE-based FL deployment.
@article{arxiv.2303.10837,
title = {FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System},
author = {Weizhao Jin and Yuhang Yao and Shanshan Han and Jiajun Gu and Carlee Joe-Wong and Srivatsan Ravi and Salman Avestimehr and Chaoyang He},
journal= {arXiv preprint arXiv:2303.10837},
year = {2024}
}