Blockchain‐Audited Federated Learning: Securing Data and Model Updates With On‐Chain Provenance

Abstract

We present a permissioned blockchain–audited federated learning (FL) framework that strengthens data provenance and model‐update integrity. Our contribution is primarily engineering and architectural: a modular two‐channel design (provenance vs. update‐audit), lightweight on‐chain validation with off‐chain analytics, and a practical mapping to the 1 + 5 architectural views. In a TensorFlow Federated + Hyperledger Fabric prototype with 10 clients, we observe ≈18% faster anomaly detection under attack and a + 0.4 pp accuracy delta versus a baseline FL setup, with ~6% communication and ~8% energy overhead. We also provide a proof‐of‐concept zero‐knowledge succinct noninteractive argument of knowledge (zk‐SNARK) flow to validate per‐client summary properties off‐chain while anchoring results on‐chain. These contributions collectively advance the practical deployment of secure, auditable FL systems.