FedBGS: A Blockchain Approach to Segment Gossip Learning in Decentralized Systems

TL;DR

FedBGS introduces a decentralized blockchain framework utilizing segmented gossip learning to enhance privacy, security, and scalability in federated learning without relying on a central server.

Contribution

The paper presents FedBGS, a novel blockchain-based decentralized system that improves privacy and security in federated learning through segmented gossip learning and blockchain integration.

Findings

Enhanced privacy and security in federated learning environments.

Decentralized architecture eliminates single point of failure.

Effective handling of non-IID data distributions.

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.