Work

Blockchain-Empowered Federated Learning: Benefits, Challenges, and Solutions

Year: 2025

Type: article

Abstract: Federated learning (FL) is a distributed machine learning approach that protects user data privacy by training models locally on clients and aggregating them on a parameter server. While effective at preserving privacy, FL systems face limitations such as single points of failure, lack of incentives, and inadequate security. To address these challenges, blockchain technology is integrated into FL systems to provide stronger security, fairness, and scalability. However, blockchain-empowered FL (BC-FL) systems introduce additional demands on network, computing, and storage resources. This survey provides a comprehensive review of recent research on BC-FL systems, analyzing the benefits and challenges associated with blockchain integration. We explore why blockchain is applicable to FL, how it can be implemented, and the challenges and existing solutions for its integration. Additionally, we offer insights on future research directions for the BC-FL system.

Source: IEEE Transactions on Big Data

DOI: 10.1109/tbdata.2025.3541560

Authors: Zeju Cai, Jianguo Chen, Yuting Fan, Zibin Zheng, Keqin Li

Institutions: Sun Yat-sen University, State University of New York, SUNY New Paltz

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

Funders: National Natural Science Foundation of China, Natural Science Foundation of Guangdong Province