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Blockchain-enabled Federated Learning: A Survey

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Version 2 2024-05-30, 16:19
Version 1 2022-05-19, 09:13
journal contribution
posted on 2024-05-30, 16:19 authored by Y Qu, MP Uddin, C Gan, Yong XiangYong Xiang, Longxiang GaoLongxiang Gao, John YearwoodJohn Yearwood
Federated learning (FL) has experienced a boom in recent years, which is jointly promoted by the prosperity of machine learning and Artificial Intelligence along with emerging privacy issues. In the FL paradigm, a central server and local end devices maintain the same model by exchanging model updates instead of raw data, with which the privacy of data stored on end devices is not directly revealed. In this way, the privacy violation caused by the growing collection of sensitive data can be mitigated. However, the performance of FL with a central server is reaching a bottleneck, while new threats are emerging simultaneously. There are various reasons, among which the most significant ones are centralized processing, data falsification, and lack of incentives. To accelerate the proliferation of FL, blockchain-enabled FL has attracted substantial attention from both academia and industry. A considerable number of novel solutions are devised to meet the emerging demands of diverse scenarios. Blockchain-enabled FL provides both theories and techniques to improve the performance of FL from various perspectives. In this survey, we will comprehensively summarize and evaluate existing variants of blockchain-enabled FL, identify the emerging challenges, and propose potentially promising research directions in this under-explored domain.

History

Journal

ACM Computing Surveys

Volume

55

Article number

ARTN 70

Pagination

1-33

Location

New York, N.Y.

Open access

  • Yes

ISSN

0360-0300

eISSN

1557-7341

Language

English

Publication classification

C1 Refereed article in a scholarly journal

Issue

4

Publisher

ASSOC COMPUTING MACHINERY