Work

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

Year: 2025

Type: article

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.

Source: IET Software

DOI: 10.1049/sfw2/6670439

Authors: Seid Mehammed, Girma Bewuketu, Demeke Getaneh, Md Nasre Alam, Shakir Khan, Fatimah Alhayan

Institutions: Woldia University, Imam Mohammad ibn Saud Islamic University, Princess Nourah bint Abdulrahman University

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,210

Funder: Princess Nourah Bint Abdulrahman University