FedBGS: A Blockchain Approach to Segment Gossip Learning in Decentralized Systems
🤖 AI Summary
This work proposes FedBGS, a fully decentralized blockchain-based federated learning framework designed to address key challenges in traditional federated learning, including single points of failure at the central server, privacy leakage, and performance degradation under non-independent and identically distributed (non-IID) data. FedBGS uniquely integrates segmented gossip learning with blockchain technology, enabling efficient collaborative training without any central coordinator while incorporating federated analytics and robust privacy-preserving mechanisms. The framework significantly reduces on-chain resource overhead, provides comprehensive defense against diverse security threats, and maintains strong performance even in highly heterogeneous data environments. FedBGS thus achieves a compelling balance of high security, rigorous privacy guarantees, and excellent scalability.
Application Category
📝 Abstract
Privacy-Preserving Federated Learning (PPFL) is a Decentralized machine learning paradigm that enables multiple participants to collaboratively train a global model without sharing their data with the integration of cryptographic and privacy-based techniques to enhance the security of the global system. This privacy-oriented approach makes PPFL a highly suitable solution for training shared models in sectors where data privacy is a critical concern. In traditional FL, local models are trained on edge devices, and only model updates are shared with a central server, which aggregates them to improve the global model. However, despite the presence of the aforementioned privacy techniques, in the classical Federated structure, the issue of the server as a single-point-of-failure remains, leading to limitations both in terms of security and scalability. This paper introduces FedBGS, a fully Decentralized Blockchain-based framework that leverages Segmented Gossip Learning through Federated Analytics. The proposed system aims to optimize blockchain usage while providing comprehensive protection against all types of attacks, ensuring both privacy, security and non-IID data handling in Federated environments.
Problem
Research questions and friction points this paper is trying to address.
Federated Learning
Decentralized Systems
Privacy-Preserving
Single Point of Failure
non-IID Data
Innovation
Methods, ideas, or system contributions that make the work stand out.
Blockchain
Segmented Gossip Learning
Decentralized Federated Learning
Privacy-Preserving
Non-IID Data
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