医疗联邦学习中实现高效低通信量的隐私保护方法
MedHE: Communication-Efficient Privacy-Preserving Federated Learning with Adaptive Gradient Sparsification for Healthcare
- 自适应梯度稀疏+密文计算，动态选关键梯度
- 通信量从1277MB降至32MB，准确率90%±0.8%
- 满足HIPAA合规，支持超百机构协同
医疗联邦学习需在资源受限的医疗机构间保障强隐私并维持计算效率。本文提出MedHE框架，结合自适应梯度稀疏与CKKS同态加密，实现对敏感医疗数据的隐私保护协同学习。通过引入误差补偿的动态阈值机制进行top-k梯度选择，通信量减少97.5%，同时保持模型性能。在环形学习误差假设下提供形式化安全分析，并实现差分隐私保证（ε ≤ 1.0）。5次独立实验显示，MedHE准确率达89.5%±0.8%，与标准联邦学习无显著差异（p=0.32），每轮训练通信量由1277MB降至32MB。全面评估表明其具备真实医疗部署可行性，支持100+机构扩展，符合HIPAA合规要求。
原文摘要 · Abstract (English)
Healthcare federated learning requires strong privacy guarantees while maintaining computational efficiency across resource-constrained medical institutions. This paper presents MedHE, a novel framework combining adaptive gradient sparsification with CKKS homomorphic encryption to enable privacy-preserving collaborative learning on sensitive medical data. Our approach introduces a dynamic threshold mechanism with error compensation for top-k gradient selection, achieving 97.5 percent communication reduction while preserving model utility. We provide formal security analysis under Ring Learning with Errors assumptions and demonstrate differential privacy guarantees with epsilon less than or equal to 1.0. Statistical testing across 5 independent trials shows MedHE achieves 89.5 percent plus or minus 0.8 percent accuracy, maintaining comparable performance to standard federated learning (p=0.32) while reducing communication from 1277 MB to 32 MB per training round. Comprehensive evaluation demonstrates practical feasibility for real-world medical deployments with HIPAA compliance and scalability to 100 plus institutions.
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