Federated Learning (FL) enables machine learning model training on distributed edge devices by aggregating local model updates rather than local data. However, privacy concerns arise as the FL server's access to local model updates can potentially reveal sensitive personal information by performing attacks like gradient inversion recovery. To address these concerns, privacy-preserving methods, such as Homomorphic Encryption (HE)-based approaches, have been proposed. Despite HE's post-quantum security advantages, its applications suffer from impractical overheads. In this paper, we present FedML-HE, the first practical system for efficient HE-based secure federated aggregation that provides a user/device-friendly deployment platform. FL-HE utilizes a novel universal overhead optimization scheme, significantly reducing both computation and communication overheads during deployment while providing customizable privacy guarantees. Our optimized system demonstrates considerable overhead reduction, particularly for large models (e.g., ~10x reduction for HE-federated training of ResNet-50 and ~40x reduction for BERT), demonstrating the potential for scalable HE-based FL deployment.

翻译：联邦学习通过在分布式边缘设备上聚合本地模型更新而非本地数据来训练机器学习模型。然而，由于联邦学习服务器能够访问本地模型更新，可能通过执行梯度反演恢复等攻击泄露敏感个人信息，从而引发隐私问题。为解决这些顾虑，研究者提出了基于同态加密的隐私保护方法。尽管同态加密具有量子安全优势，但其应用常因不可忽视的开销而受限。本文提出FedML-HE，这是首个用于高效同态加密安全联邦聚合的实用系统，能够提供用户/设备友好的部署平台。该系统采用新颖的通用开销优化方案，在部署过程中显著降低计算与通信开销，同时提供可定制的隐私保障。经优化的系统在大模型上展现了可观的开销缩减（例如，ResNet-50的同态加密联邦训练约降低10倍，BERT约降低40倍），验证了可扩展的同态加密联邦学习部署潜力。