IEEE Computational Intelligence Magazine - August 2022 - 26
When Federated Learning
Meets Blockchain: A New
Distributed Learning Paradigm
Chuan Ma, Jun Li, and Long Shi
Nanjing University of Science and Technology, CHINA
Ming Ding
CSIRO, AUSTRALIA
Taotao Wang
Shenzhen University, CHINA
Zhu Han
University of Houston, USA, and Kyung Hee University,
SOUTH KOREA
H. Vincent Poor
Princeton University, USA
Digital Object Identifier 10.1109/MCI.2022.3180932
Date of current version: 19 July 2022
26 IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | AUGUST 2022
Abstract-Motivated by the increasingly powerful computing
capabilities of end-user equipment, and by the growing privacy
concerns over sharing sensitive raw data, a distributed
machine learning paradigm known as 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 conventional FL framework relies
heavily on a single central server, and it may fail if such a
server behaves maliciously. To address this single point of failure,
in this work, a blockchain-assisted decentralized FL
framework is investigated, which can prevent malicious clients
from poisoning the learning process, and thus provides a selfmotivated
and reliable learning environment for clients. In
Corresponding author: Jun Li (e-mail: jun.li@njust.edu.cn).
1556-603X/22©2022IEEE
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IEEE Computational Intelligence Magazine - August 2022
Table of Contents for the Digital Edition of IEEE Computational Intelligence Magazine - August 2022
Contents
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