Cross-silo federated learning (FL) enables multiple clients to collaboratively train a machine learning model without sharing training data, but privacy in FL remains a major challenge. Techniques using homomorphic encryption (HE) have been designed to solve this but bring their own challenges. Many techniques using single-key HE (SKHE) require clients to fully trust each other to prevent privacy disclosure between clients. However, fully trusted clients are hard to ensure in practice. Other techniques using multi-key HE (MKHE) aim to protect privacy from untrusted clients but lead to the disclosure of training results in public channels by untrusted third parties, e.g., the public cloud server. Besides, MKHE has higher computation and communication complexity compared with SKHE. We present a new FL protocol ESAFL that leverages a novel efficient and secure additively HE (ESHE) based on the hard problem of ring learning with errors. ESAFL can ensure the security of training data between untrusted clients and protect the training results against untrusted third parties. In addition, theoretical analyses present that ESAFL outperforms current techniques using MKHE in computation and communication, and intensive experiments show that ESAFL achieves approximate 204 times-953 times and 11 times-14 times training speedup while reducing the communication burden by 77 times-109 times and 1.25 times-2 times compared with the state-of-the-art FL models using SKHE.

翻译：跨孤岛联邦学习允许多方在不共享训练数据的前提下协作训练机器学习模型，但联邦学习中的隐私保护仍是一项重大挑战。基于同态加密的技术虽被设计用于解决该问题，却引入了新的困难。使用单密钥同态加密的多数方案要求客户端完全相互信任，以防止客户端间的隐私泄露，然而实践中很难确保客户端完全可信。采用多密钥同态加密的其他方案旨在保护客户端免受不可信方的隐私窥探，却导致训练结果在公共信道中暴露给不可信的第三方（例如公共云服务器）。此外，多密钥同态加密相比单密钥同态加密具有更高的计算和通信复杂度。本文提出一种新型联邦学习协议ESAFL，该协议基于环带误差学习问题的困难性，利用一种新型高效安全加法同态加密机制。ESAFL能够确保不可信客户端之间训练数据的安全性，并保护训练结果免受不可信第三方的窥探。理论分析表明，ESAFL在计算与通信效率上优于当前基于多密钥同态加密的方案；密集实验显示，与当前最优的基于单密钥同态加密的联邦学习模型相比，ESAFL在降低77倍-109倍与1.25倍-2倍通信开销的同时，实现了约204倍-953倍及11倍-14倍的训练加速。