xiang-blockchainenabled-accepted-2022.pdf (35.07 MB)
Download file

Blockchain-Enabled Federated Learning: A Survey

Download (35.07 MB)
journal contribution
posted on 2022-01-01, 00:00 authored by Youyang Qu, Md Palash UddinMd Palash Uddin, Chenquan Gan, Yong XiangYong Xiang, Longxiang Gao, John YearwoodJohn Yearwood
Federated learning (FL) is experiencing fast booming in recent years, which is jointly promoted by the prosperity of machine learning and Artificial Intelligence along with the 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 performances 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

Pagination

1 - 33

Publisher

Association for Computing Machinery (ACM)

Location

New York, N.Y.

ISSN

0360-0300

eISSN

1557-7341

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Usage metrics

Read the peer-reviewed publication

Categories

Exports