# ScaleSFL: A Sharding Solution for Blockchain-Based Federated Learning

arXiv: https://arxiv.org/abs/2204.01202 · HTML: https://mlsanity.com/paper/2204.01202/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2022.

## Built on by (most cited first, 4 of 4)

- [IronForge: An Open, Secure, Fair, Decentralized Federated Learning](https://mlsanity.com/paper/2301.04006/) (2023)
- [AutoDFL: A Scalable and Automated Reputation-Aware Decentralized Federated Learning](https://mlsanity.com/paper/2501.04331/) (2025)
- [FedBlock: A Blockchain Approach to Federated Learning against Backdoor Attacks](https://mlsanity.com/paper/2411.02773/) (2024)
- [DAG-AFL:Directed Acyclic Graph-based Asynchronous Federated Learning](https://mlsanity.com/paper/2507.20571/) (2025)

## Abstract (arXiv)

Blockchain-based federated learning has gained significant interest over the last few years with the increasing concern for data privacy, advances in machine learning, and blockchain innovation. However, gaps in security and scalability hinder the development of real-world applications. In this study, we propose ScaleSFL, which is a scalable blockchain-based sharding solution for federated learning. ScaleSFL supports interoperability by separating the off-chain federated learning component in order to verify model updates instead of controlling the entire federated learning flow. We implemented ScaleSFL as a proof-of-concept prototype system using Hyperledger Fabric to demonstrate the feasibility of the solution. We present a performance evaluation of results collected through Hyperledger Caliper benchmarking tools conducted on model creation. Our evaluation results show that sharding can improve validation performance linearly while remaining efficient and secure.