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

• URL: http://arxiv.org/abs/2310.19287v1
• Date: Mon, 30 Oct 2023 06:05:50 GMT
• Title: Enhancing Scalability and Reliability in Semi-Decentralized Federated Learning With Blockchain: Trust Penalization and Asynchronous Functionality
• Authors: Ajay Kumar Shrestha, Faijan Ahamad Khan, Mohammed Afaan Shaikh, Amir Jaberzadeh and Jason Geng
• Abstract summary: The paper focuses on enhancing the trustworthiness of participating nodes through a trust penalization mechanism. The proposed system aims to create a fair, secure and transparent environment for collaborative machine learning without compromising data privacy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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