Blockchain and Trustworthy Reputation for Federated Learning: Opportunities and Challenges

Abstract

In the domain of Collaborative Artificial Intelligence, Federated Learning ($\mathbf{F L}$) is a technique that enables multiple entities to collaboratively refine AI models while adhering to stringent data privacy standards, without the need for direct data sharing. This paper explores the integration of blockchain technology with FL to establish reliable trust mechanisms within this collaborative framework. We highlight and review current blockchain-enabled reputation mechanisms that evaluate the reliability and quality of contributions from participants, which are crucial for maintaining trust and operational integrity in distributed settings. Through our review, we address the concept and implementation challenges. Additionally, we discuss recent technological advances and explore the emerging opportunities that blockchain presents to address trust-related challenges in FL, emphasizing significant prospects for future research directions, such as decentralized identities, zero trust, and zero-knowledge proofs to enhance trust in these environments.