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Blockchain for federated learning toward secure distributed machine learning systems: a systemic survey
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Blockchain for federated learning toward secure distributed machine learning systems: a systemic survey
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Journal Article
Blockchain for federated learning toward secure distributed machine learning systems: a systemic survey
2022
Overview
Federated learning (
FL
) is a promising decentralized deep learning technology, which allows users to update models cooperatively without sharing their data.
FL
is reshaping existing industry paradigms for mathematical modeling and analysis, enabling an increasing number of industries to build privacy-preserving, secure distributed machine learning models. However, the inherent characteristics of
FL
have led to problems such as privacy protection, communication cost, systems heterogeneity, and unreliability model upload in actual operation. Interestingly, the integration with Blockchain technology provides an opportunity to further improve the
FL
security and performance, besides increasing its scope of applications. Therefore, we denote this integration of Blockchain and
FL
as the Blockchain-based federated learning (
BCFL
) framework. This paper introduces an in-depth survey of
BCFL
and discusses the insights of such a new paradigm. In particular, we first briefly introduce the
FL
technology and discuss the challenges faced by such technology. Then, we summarize the Blockchain ecosystem. Next, we highlight the structural design and platform of
BCFL
. Furthermore, we present the attempts ins improving
FL
performance with Blockchain and several combined applications of incentive mechanisms in
FL
. Finally, we summarize the industrial application scenarios of
BCFL
.
Publisher
Springer Berlin Heidelberg,Springer Nature B.V
Subject
