Invited Paper: A Verifiable and Adaptive Federated Learning Framework via Zero-Knowledge Proofs and Reputation-Weighted Blockchain

Abstract

This article addresses the security of Federated Learning (FL) in distributed systems against a range of attacks, including model poisoning and unverifiable client behavior, while ensuring the semantic correctness of gradient updates. It proposes ZK-FedLedger, a verifiable and adaptive FL framework that integrates multi-constraint zero-knowledge proofs with a reputation-weighted Byzantine fault-tolerant blockchain consensus. Each client generates a zk-SNARK proof certifying that its update satisfies both an adaptive norm bound and a geometric alignment constraint relative to a trusted reference gradient. Verified commitments are recorded on-chain, while model parameters are aggregated off-chain using a hybrid storage architecture that minimizes blockchain overhead. Experimental evaluation on MNIST demonstrates stable convergence, with test accuracies of 98.17% (IID) and 94.93% (Non-IID), and near-perfect detection of major poisoning attacks. The results show that ZK-FedLedger enables proactive, cryptographically verifiable FL without compromising scalability or model performance.