# Blockchain-based Trustworthy Federated Learning Architecture

arXiv: https://arxiv.org/abs/2108.06912 · HTML: https://mlsanity.com/paper/2108.06912/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 33 references that are in the map.
- First posted on arXiv 16 Aug 2021.

## Builds on (most cited first, 13 of 13)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Agnostic Federated Learning](https://mlsanity.com/paper/1902.00146/) (2019)
- [Robust and Communication-Efficient Federated Learning from Non-IID Data](https://mlsanity.com/paper/1903.02891/) (2019)
- [GhostNet: More Features from Cheap Operations](https://mlsanity.com/paper/1911.11907/) (2019)
- [Blockchained On-Device Federated Learning](https://mlsanity.com/paper/1808.03949/) (2018)
- [Reliable Federated Learning for Mobile Networks](https://mlsanity.com/paper/1910.06837/) (2019)
- [Fairness-aware Agnostic Federated Learning](https://mlsanity.com/paper/2010.05057/) (2020)
- [Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imaging](https://mlsanity.com/paper/2007.06537/) (2020)
- [Dynamic Fusion based Federated Learning for COVID-19 Detection](https://mlsanity.com/paper/2009.10401/) (2020)
- [Experiments of Federated Learning for COVID-19 Chest X-ray Images](https://mlsanity.com/paper/2007.05592/) (2020)
- [Architectural Patterns for the Design of Federated Learning Systems](https://mlsanity.com/paper/2101.02373/) (2021)
- [FLRA: A Reference Architecture for Federated Learning Systems](https://mlsanity.com/paper/2106.11570/) (2021)
- [A Blockchain-based Approach for Data Accountability and Provenance Tracking](https://mlsanity.com/paper/1706.04507/) (2017)

## Built on by (most cited first, 5 of 5)

- [Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering](https://mlsanity.com/paper/2209.04963/) (2022)
- [Towards Verifiable Federated Learning](https://mlsanity.com/paper/2202.08310/) (2022)
- [Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning](https://mlsanity.com/paper/2403.19178/) (2024)
- [Trustworthy Federated Learning: A Survey](https://mlsanity.com/paper/2305.11537/) (2023)
- [End-to-End Verifiable Decentralized Federated Learning](https://mlsanity.com/paper/2404.12623/) (2024)

## Abstract (arXiv)

Federated learning is an emerging privacy-preserving AI technique where clients (i.e., organisations or devices) train models locally and formulate a global model based on the local model updates without transferring local data externally. However, federated learning systems struggle to achieve trustworthiness and embody responsible AI principles. In particular, federated learning systems face accountability and fairness challenges due to multi-stakeholder involvement and heterogeneity in client data distribution. To enhance the accountability and fairness of federated learning systems, we present a blockchain-based trustworthy federated learning architecture. We first design a smart contract-based data-model provenance registry to enable accountability. Additionally, we propose a weighted fair data sampler algorithm to enhance fairness in training data. We evaluate the proposed approach using a COVID-19 X-ray detection use case. The evaluation results show that the approach is feasible to enable accountability and improve fairness. The proposed algorithm can achieve better performance than the default federated learning setting in terms of the model's generalisation and accuracy.