When Federated Learning Meets Blockchain: A New Distributed Learning Paradigm

TL;DR

This paper introduces BLADE-FL, a blockchain-based decentralized federated learning framework that enhances privacy and security by removing reliance on a central server and integrating model aggregation with blockchain mining.

Contribution

It proposes a novel decentralized FL framework using blockchain to prevent malicious attacks and ensure reliable, self-motivated learning environments.

Findings

Decentralized model aggregation enhances security against malicious clients.

Blockchain integration provides a tamper-proof record of model updates.

Experimental results demonstrate improved robustness and privacy protection.

Abstract

Motivated by the explosive computing capabilities at end user equipments, as well as the growing privacy concerns over sharing sensitive raw data, a new machine learning paradigm, named federated learning (FL) has emerged. By training models locally at each client and aggregating learning models at a central server, FL has the capability to avoid sharing data directly, thereby reducing privacy leakage. However, the traditional FL framework heavily relies on a single central server and may fall apart if such a server behaves maliciously. To address this single point of failure issue, this work investigates a blockchain assisted decentralized FL (BLADE-FL) framework, which can well prevent the malicious clients from poisoning the learning process, and further provides a self-motivated and reliable learning environment for clients. In detail, the model aggregation process is fully decentralized and the tasks of training for FL and mining for blockchain are integrated into each participant. In addition, we investigate the unique issues in this framework and provide analytical and experimental results to shed light on possible solutions.