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The paper presents an innovative approach to address the challenges ofscalability and reliability in Distributed Federated Learning by leveraging theintegration of blockchain technology. The paper focuses on enhancing thetrustworthiness of participating nodes through a trust penalization mechanismwhile also enabling asynchronous functionality for efficient and robust modelupdates. By combining Semi-Decentralized Federated Learning with Blockchain(SDFL-B), the proposed system aims to create a fair, secure and transparentenvironment for collaborative machine learning without compromising dataprivacy. The research presents a comprehensive system architecture,methodologies, experimental results, and discussions that demonstrate theadvantages of this novel approach in fostering scalable and reliable SDFL-Bsystems.

Enhancing Scalability and Reliability in Semi-Decentralized Federated Learning With Blockchain: Trust Penalization and Asynchronous Functionality