Pro-ZkFL: Efficient Verifiable Federated Learning Based on Probabilistic Zero-Knowledge Auditing and Reputation Sharding

Abstract

Federated Learning (FL) enables distributed model training while preserving data privacy; however, it remains vulnerable to poisoning attacks and lacks computational integrity. Recent solutions integrating Zero-Knowledge Proofs (ZKPs) and blockchain have successfully established process-level verifiability but suffer from prohibitive computational overhead due to the requirement of generating cryptographic proofs for every local update. To address this efficiency bottleneck, this paper proposes Pro-ZkFL, a reputation-aware probabilistic verification framework. Unlike deterministic approaches that verify every transaction, Pro-ZkFL utilizes Verifiable Random Functions (VRF) on-chain to dynamically select a subset of clients for auditing based on their historical reputation scores. We design a dual-commitment scheme where clients submit lightweight cryptographic commitments for every round but generate heavy ZKPs only when challenged. Experimental results on FashionMNIST and CIFAR-10 demonstrate that Pro-ZkFL reduces computational overhead by approximately 82 % and gas costs by 73 % compared to full-verification baselines while maintaining a 99 % detection rate against persistent adversaries, offering a scalable trade-off between security and efficiency.