用混合加密提升联邦学习的隐私与效率，让多方协作更安全可扩展。
A Privacy-Centric Approach: Scalable and Secure Federated Learning Enabled by Hybrid Homomorphic Encryption
- 结合对称加密与同态加密，降低计算和通信开销
- 在保持数据隐私的前提下显著减少通信成本
- 适合医疗、金融等高隐私要求的分布式学习场景
联邦学习（FL）允许在不共享原始数据的情况下协同训练模型，是隐私敏感领域有前景的解决方案。尽管潜力巨大，FL仍面临通信开销大和数据隐私保护难的问题。隐私保护技术（PPTs）如同态加密（HE）虽能缓解这些担忧，但带来显著的计算与通信成本，限制了实际应用。本文探讨将混合同态加密（HHE）——一种结合对称加密与同态加密的密码协议——有效集成到联邦学习中，以同时应对通信与隐私挑战，为构建可扩展、安全的去中心化学习系统铺平道路。
原文摘要 · Abstract (English)
Federated Learning (FL) enables collaborative model training without sharing raw data, making it a promising approach for privacy-sensitive domains. Despite its potential, FL faces significant challenges, particularly in terms of communication overhead and data privacy. Privacy-preserving Techniques (PPTs) such as Homomorphic Encryption (HE) have been used to mitigate these concerns. However, these techniques introduce substantial computational and communication costs, limiting their practical deployment. In this work, we explore how Hybrid Homomorphic Encryption (HHE), a cryptographic protocol that combines symmetric encryption with HE, can be effectively integrated with FL to address both communication and privacy challenges, paving the way for scalable and secure decentralized learning system.
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