MedHE: Communication-Efficient Privacy-Preserving Federated Learning with Adaptive Gradient Sparsification for Healthcare
It addresses privacy and communication bottlenecks for resource-constrained medical institutions, offering a practical solution for HIPAA-compliant deployments, though it builds incrementally on existing techniques.
This paper tackles the challenge of maintaining privacy and efficiency in healthcare federated learning by proposing MedHE, a framework that combines adaptive gradient sparsification with homomorphic encryption, achieving a 97.5% communication reduction and 89.5% accuracy while ensuring differential privacy with epsilon ≤ 1.0.
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.