Robust Blockchain-Based Federated Learning

Aftab Akram, Clémentine Gritti, Mohd Hazali Mohamed Halip, Mohd Hazali Mohamed Halip, Nur Diyana Kamarudin, Nur Diyana Kamarudin, Marini Mansor, Syarifah Bahiyah Rahayu, Syarifah Bahiyah Rahayu, Melek Önen

2025

Abstract

In Federated Learning (FL), clients collaboratively train a global model by updating it locally. Secure Aggregation (SA) techniques ensure that individual client updates remain protected, allowing only the global model to be revealed while keeping the individual updates private. These updates are usually protected through expensive cryptographic techniques such as homomorphic encryption or multi-party computation. We propose a new solution that leverages blockchain technology, specifically the Secret Network (SN), to provide privacy-preserving aggregation with aggregate integrity through Smart Contracts in Trusted Execution Environments (TEEs). Moreover, FL systems face the risk of Byzantine clients submitting poisoned updates, which can degrade the model performance. To counter this, we integrate three state-of-the-art robust aggregation techniques within the Smart Contract, namely Krum, Trim Mean and Median. Furthermore, we have evaluated the performance of our framework which remains efficient in terms of computation and communication costs. We have also exhibited similar accuracy results compared to state-of-the art scheme named SABLE.

Paper Citation

in Harvard Style

Akram A., Gritti C., Halip M., Kamarudin N., Mansor M., Rahayu S. and Önen M. (2025). Robust Blockchain-Based Federated Learning. In Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP; ISBN 978-989-758-735-1, SciTePress, pages 59-70. DOI: 10.5220/0013188800003899

in Bibtex Style

@conference{icissp25,
author={Aftab Akram and Clémentine Gritti and Mohd Halip and Nur Kamarudin and Marini Mansor and Syarifah Rahayu and Melek Önen},
title={Robust Blockchain-Based Federated Learning},
booktitle={Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP},
year={2025},
pages={59-70},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013188800003899},
isbn={978-989-758-735-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP
TI - Robust Blockchain-Based Federated Learning
SN - 978-989-758-735-1
AU - Akram A.
AU - Gritti C.
AU - Halip M.
AU - Kamarudin N.
AU - Mansor M.
AU - Rahayu S.
AU - Önen M.
PY - 2025
SP - 59
EP - 70
DO - 10.5220/0013188800003899
PB - SciTePress