Open Access

ARTICLE

Asynchronous Tiered Federated Learning Storage Scheme Based on Blockchain and IPFS

Tianyu Li¹, Dezhi Han¹, Jiatao Li¹, Kuan-Ching Li^2,*

1 College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, China
2 Department of Computer Science and Information Engineering, Providence University, Taichung City, 43301, Taiwan

* Corresponding Author: Kuan-Ching Li. Email:

Computers, Materials & Continua 2025, 83(3), 4117-4140. https://doi.org/10.32604/cmc.2025.063630

Received 20 January 2025; Accepted 31 March 2025; Issue published 19 May 2025

Abstract

As is known, centralized federated learning faces risks of a single point of failure and privacy breaches, and blockchain-based federated learning frameworks can address these challenges to a certain extent in recent works. However, malicious clients may still illegally access the blockchain to upload malicious data or steal on-chain data. In addition, blockchain-based federated training suffers from a heavy storage burden and excessive network communication overhead. To address these issues, we propose an asynchronous, tiered federated learning storage scheme based on blockchain and IPFS. It manages the execution of federated learning tasks through smart contracts deployed on the blockchain, decentralizing the entire training process. Additionally, the scheme employs a secure and efficient blockchain-based asynchronous tiered architecture, integrating attribute-based access control technology for resource exchange between the clients and the blockchain network. It dynamically manages access control policies during training and adopts a hybrid data storage strategy combining blockchain and IPFS. Experiments with multiple sets of image classification tasks are conducted, indicating that the storage strategy used in this scheme saves nearly 50 percent of the communication overhead and significantly reduces the on-chain storage burden compared to the traditional blockchain-only storage strategy. In terms of training effectiveness, it maintains similar accuracy as centralized training and minimizes the probability of being attacked.

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Keywords

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