探索提升联邦学习隐私与效率的新范式
Emerging Paradigms for Securing Federated Learning Systems
- 对比分析可信执行环境、物理不可克隆函数等六类新兴安全技术
- 指出传统加密方法计算开销大、扩展性差的瓶颈问题
- 适合关注联邦学习安全架构的科研人员与系统设计者
联邦学习（FL）在保持原始数据去中心化的同时实现协同模型训练，为利用物联网设备潜力并保障本地数据隐私提供了有效途径。然而，现有隐私保护技术面临显著挑战：多方计算（MPC）、同态加密（HE）和差分隐私（DP）通常导致高计算成本且可扩展性有限。本文综述了有望提升联邦学习中隐私与效率的新兴范式，包括可信执行环境（TEEs）、物理不可克隆函数（PUFs）、量子计算（QC）、混沌加密（CBE）、类脑计算（NC）和群体智能（SI）。针对每种范式，评估其在联邦学习流程中的适用性，分析其优势、局限及实际部署考量。最后，总结开放性挑战与未来研究方向，为构建安全且可扩展的联邦学习系统提供详细路线图。
原文摘要 · Abstract (English)
Federated Learning (FL) facilitates collaborative model training while keeping raw data decentralized, making it a conduit for leveraging the power of IoT devices while maintaining privacy of the locally collected data. However, existing privacy- preserving techniques present notable hurdles. Methods such as Multi-Party Computation (MPC), Homomorphic Encryption (HE), and Differential Privacy (DP) often incur high compu- tational costs and suffer from limited scalability. This survey examines emerging approaches that hold promise for enhancing both privacy and efficiency in FL, including Trusted Execution Environments (TEEs), Physical Unclonable Functions (PUFs), Quantum Computing (QC), Chaos-Based Encryption (CBE), Neuromorphic Computing (NC), and Swarm Intelligence (SI). For each paradigm, we assess its relevance to the FL pipeline, outlining its strengths, limitations, and practical considerations. We conclude by highlighting open challenges and prospective research avenues, offering a detailed roadmap for advancing secure and scalable FL systems.
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