标题： 一种以隐私为中心的方法：由混合同态加密支持的可扩展且安全的联邦学习 显示英文标题

标题： A Privacy-Centric Approach: Scalable and Secure Federated Learning Enabled by Hybrid Homomorphic Encryption

摘要： 联邦学习（FL）能够在不共享原始数据的情况下进行协作模型训练，使其成为隐私敏感领域的一种有前景的方法。 尽管具有潜力，FL在通信开销和数据隐私方面面临重大挑战。 用于保护隐私的技术（PPTs）如同态加密（HE）已被用来缓解这些担忧。 然而，这些技术会引入大量的计算和通信成本，限制了其实际部署。 在本工作中，我们探讨如何有效地将混合同态加密（HHE）与FL集成，HHE是一种结合对称加密和HE的密码协议，以解决通信和隐私挑战，为可扩展且安全的去中心化学习系统铺平道路。 显示英文摘要

摘要： 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.