在医疗联邦学习中实现安全聚合，保护隐私且不影响模型精度。
Enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare Applications
- 对比两种安全聚合协议，评估其在医疗数据上的适用性。
- 计算开销低于1%（CPU）和50%（GPU），保护阶段<10秒。
- 隐私保护效果显著，模型准确率下降不超过2%，适合敏感场景。
在真实世界场景中部署联邦学习（FL），特别是在医疗领域，面临通信与安全挑战。针对联邦聚合过程中的隐私保护问题，研究聚焦于安全聚合（SA）方案，以保障客户端传输的模型参数隐私。然而，现有联邦学习框架中SA的实际应用受限于计算与通信瓶颈。本研究在开源Fed-BioMed框架中实现并比较了两种SA协议：Joye-Libert（JL）与低开销掩码（LOM），在四个医疗数据分析任务上进行了全面基准测试。理论与实验评估表明，SA协议能有效保护隐私，同时保持任务准确性。训练时计算开销在CPU上低于1%，在大模型下GPU上低于50%，保护阶段耗时不足10秒。与非SA场景相比，引入SA对任务准确率影响不超过2%。研究证明了SA在真实医疗应用中的可行性，推动了隐私保护技术在敏感场景中的落地。
原文摘要 · Abstract (English)
Deploying federated learning (FL) in real-world scenarios, particularly in healthcare, poses challenges in communication and security. In particular, with respect to the federated aggregation procedure, researchers have been focusing on the study of secure aggregation (SA) schemes to provide privacy guarantees over the model's parameters transmitted by the clients. Nevertheless, the practical availability of SA in currently available FL frameworks is currently limited, due to computational and communication bottlenecks. To fill this gap, this study explores the implementation of SA within the open-source Fed-BioMed framework. We implement and compare two SA protocols, Joye-Libert (JL) and Low Overhead Masking (LOM), by providing extensive benchmarks in a panel of healthcare data analysis problems. Our theoretical and experimental evaluations on four datasets demonstrate that SA protocols effectively protect privacy while maintaining task accuracy. Computational overhead during training is less than 1% on a CPU and less than 50% on a GPU for large models, with protection phases taking less than 10 seconds. Incorporating SA into Fed-BioMed impacts task accuracy by no more than 2% compared to non-SA scenarios. Overall this study demonstrates the feasibility of SA in real-world healthcare applications and contributes in reducing the gap towards the adoption of privacy-preserving technologies in sensitive applications.
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