GSSFL : A Group Signature and Smart Contract‐Based Framework for Privacy‐Preserving Federated Learning

ABSTRACT Federated learning has emerged as a powerful paradigm for collaborative machine learning across multiple parties, holding considerable potential for modern industries. However, its inherently decentralised and collaborative nature raises critical concerns about data security and user privacy. Sensitive information—such as user preferences, behaviours, and identities—remains susceptible to inference attacks, revealing the limitations of conventional privacy‐preserving techniques in existing federated learning frameworks. To address these challenges, this paper presents GSSFL, a novel federated learning architecture that integrates smart contracts and group signatures to enhance both privacy protection and system trustworthiness. GSSFL enables secure and verifiable data exchange without compromising user anonymity, while its decentralised design encourages broader participation in federated learning processes. Experimental results demonstrate that GSSFL effectively satisfies the demands of privacy‐preserving data sharing with minimal performance overhead.