Trusted Aggregation for Decentralized Federated Learning in Healthcare Consumer Electronics Using Zero-Knowledge Proofs
Аннотация
The increasing use of federated learning (FL) in healthcare IoT demands rigorous verification to ensure the correctness of remote model training without compromising patient data privacy. However, existing approaches either assume full trust in clients or introduce high computational and communication costs when integrating cryptographic guarantees. In this work, we propose a lightweight, privacy-preserving federated learning framework that integrates zk-SNARK-based verifiable training over a ring topology. Our system ensures that each client’s model update and aggregation step can be independently verified without revealing sensitive data or requiring a central auditor. We design an efficient proof composition strategy (CGro16) tailored for chained convolution operations and commitment schemes optimized for healthcare models. We also introduce a matrix polynomial-based masking mechanism (MatProofs) to support zero-knowledge commitments for convolutional neural networks (CNNs).Experimental results on standard benchmarks (MNIST, CIFAR-100) show up to 47% reduction in proof generation time and 39% lower memory overhead compared to baseline zk-SNARK schemes. The protocol is also benchmarked on edge devices (Jetson Nano, Raspberry Pi), confirming its suitability for remote and wearable healthcare scenarios.