TeleZK-FL: enabling trustless and verifiable remote patient monitoring via quantized zero-knowledge federated learning
TL;DR
TeleZK-FL establishes the feasibility of verifiable, trustless federated learning on commodity telehealth hardware by eliminating the computational bottlenecks of server-side proof generation while incurring only 0.1%–0.3% AUC degradation.
Abstract
Background The rapid expansion of Internet of Medical Things (IoMT) and telehealth platforms has generated vast amounts of patient data suitable for training diagnostic Artificial Intelligence (AI) models. However, strict privacy regulations (HIPAA, GDPR) and the risk of data breaches prevent the centralization of this sensitive information. While Federated Learning (FL) allows for collaborative training without sharing raw patient data, it introduces a critical “trust deficit”: central aggregators cannot verify the integrity of local model updates without inspecting the private data, leaving the system vulnerable to model poisoning and malicious actors. Methods We introduce TeleZK-FL, a privacy-preserving framework designed specifically for resource-constrained telehealth environments. Unlike existing Zero-Knowledge Proof (ZKP) systems that require high-performance computing or offload proof generation to trusted edge servers, TeleZK-FL integrates (1) Post-Training Quantization (PTQ) to compress model gradients from 32-bit floating-point to 8-bit integers, and (2) optimized Look-Up Table (LUT) arguments to generate cryptographic proofs of client-side training integrity directly on edge devices. We evaluated the framework on two clinical modalities—CheXpert (chest X-rays) and PTB-XL (12-lead ECGs)—using simulated medical edge gateways (Raspberry Pi 4) under both homogeneous and heterogeneous data distributions. Results TeleZK-FL accelerates cryptographic proof generation by approximately 25× compared to standard ZK-SNARK implementations, generating full-model proofs in approximately 84 milliseconds per client on edge hardware. It cuts the communication payload by 75% (a 4.0× reduction) while maintaining a diagnostic Area Under the Curve (AUC) of 0.877 on CheXpert and 0.891 on PTB-XL, representing a degradation of only 0.1% and 0.3% respectively compared to the unquantized baselines of 0.878 and 0.894. Conclusion TeleZK-FL establishes the feasibility of verifiable, trustless federated learning on commodity telehealth hardware. By eliminating the computational bottlenecks of server-side proof generation while incurring only 0.1%–0.3% AUC degradation, it provides an efficient, regulation-aligned method for building scalable and secure decentralized medical AI networks. We further discuss an inherent trade-off: the edge-efficient KZG-based construction is classically—not post-quantum—secure, which we position explicitly against recent lattice-based alternatives.