Enhancing Scalability and Reliability in Semi-Decentralized Federated
  Learning With Blockchain: Trust Penalization and Asynchronous Functionality
Ajay Kumar Shrestha Faijan Ahamad Khan Mohammed Afaan Shaikh Amir Jaberzadeh Jason Geng
The paper presents an innovative approach to address the challenges of
scalability and reliability in Distributed Federated Learning by leveraging the
integration of blockchain technology. The paper focuses on enhancing the
trustworthiness of participating nodes through a trust penalization mechanism
while also enabling asynchronous functionality for efficient and robust model
updates. By combining Semi-Decentralized Federated Learning with Blockchain
(SDFL-B), the proposed system aims to create a fair, secure and transparent
environment for collaborative machine learning without compromising data
privacy. The research presents a comprehensive system architecture,
methodologies, experimental results, and discussions that demonstrate the
advantages of this novel approach in fostering scalable and reliable SDFL-B
systems.