Blockchain Empowered Federated Learning

Abstract

In this chapter, we embark on a journey through the dynamic intersection of blockchain technology and federated machine learning (FML). This chapter elucidates the pivotal role of FML in mitigating data privacy concerns in the ever-expanding field of Artificial Intelligence. This chapter begins by unveiling the intrinsic challenges stemming from centralized data collection and traditional machine learning (ML) methods. It emphasizes the urgent need for innovative solutions that not only enhance learning efficiency but also ensure the confidentiality and security of sensitive data. FML emerges as a promising paradigm where disparate parties collaboratively train ML models without the necessity of centralized data aggregation. This chapter underscores the merits of FML, which encompass data privacy preservation and efficient model training. However, it doesn&s;t shy away from exposing the stumbling blocks encountered, including centralization issues, potential adversarial updates, and the overarching data privacy dilemma. To address these challenges, this chapter introduces blockchain technology as a robust foundation. Ethereum and smart contracts (SCs) take center stage as they empower a decentralized framework for federated learning. An SC assumes the role of a coordinator, ensuring the secure aggregation of model updates. Furthermore, the innovative application of zero-knowledge STARK proofs is employed to verify the legitimacy of client training processes, thus upholding the integrity of data. This chapter concludes by evaluating the performance of this blockchain-facilitated federated learning model, showcasing promising results that are in close proximity to theoretical limits. However, it also highlights trade-offs, including increased time requirements and gas costs associated with blockchain integration. In sum, this chapter sets the stage for an exciting exploration of how blockchain technology can empower federated learning, transforming it into a robust, secure, and efficient approach to ML while safeguarding data privacy. It opens the door to further research and innovation in this rapidly evolving and highly relevant field.