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Blockchain for federated learning toward secure distributed machine learning systems: a systemic survey.

Dun Li¹, Dezhi Han¹, Tien-Hsiung Weng²

¹College of Information Engineering at Shanghai Maritime University, Pudong, China.

|November 29, 2021

Summary

This summary is machine-generated.

Federated learning (FL) enables collaborative model training without data sharing. Integrating Blockchain with FL, creating Blockchain-based federated learning (BCFL), enhances security and performance for distributed machine learning.

Keywords:

Blockchain Federated learning Incentive mechanism Industrial Applications Smart Contract

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Area of Science:

Computer Science
Artificial Intelligence
Cybersecurity

Background:

Federated learning (FL) is a decentralized deep learning approach enabling cooperative model updates without direct data sharing.
FL offers privacy-preserving and secure distributed machine learning, transforming mathematical modeling across industries.
Challenges in FL include privacy concerns, high communication costs, system heterogeneity, and unreliable model uploads.

Purpose of the Study:

To introduce and survey the Blockchain-based federated learning (BCFL) framework, an integration of Blockchain and FL.
To discuss the insights and potential of this new paradigm for enhancing FL security and performance.
To explore structural designs, platforms, and applications of BCFL, including incentive mechanisms.

Main Methods:

A comprehensive survey of existing literature on Federated Learning and Blockchain technology.
Analysis of the challenges inherent in FL operations.
Exploration of Blockchain's role in mitigating FL challenges and enhancing its capabilities.

Main Results:

The integration of Blockchain technology with FL (BCFL) addresses key FL challenges like privacy, cost, and reliability.
BCFL frameworks offer improved security, performance, and expanded application scope for distributed machine learning.
The survey highlights structural designs, platforms, and incentive mechanisms within the BCFL paradigm.

Conclusions:

Blockchain-based federated learning (BCFL) presents a robust solution to enhance the security, efficiency, and applicability of federated learning.
BCFL is poised to enable more secure and performant privacy-preserving machine learning across various industrial scenarios.
Further research and development in BCFL are crucial for realizing its full potential in decentralized AI.