Federated Reinforcement Learning
Federated Reinforcement Learning is a machine learning framework that integrates reinforcement learning with federated learning principles to enable decentralized agents to collaboratively learn shared control policies while preserving data locality and privacy.
162
Publications
10.3K
Citations
620
Authors
276
Institutions
Publications per year
2017–2026
162
Parents
162
Optimizing Federated Learning on Non-IID Data with Reinforcement Learning
Hao Wang, Zakhary Kaplan, Di Niu et al. · 2020 · 958 citations
A state-of-the-art survey on solving non-IID data in Federated Learning
Xiaodong Ma, Jia Zhu, Zhihao Lin et al. · Future Generation Computer Systems · 2022 · 362 citations
1–5 of 162
620
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276

University of Oulu
Oulu, Finland
11 h-index
20 publications
1.3K citations

King Saud University
Riyadh, Saudi Arabia
7 h-index
9 publications
712 citations

Beijing University of Posts and Telecommunications
Beijing, China
6 h-index
21 publications
864 citations

University of Electronic Science and Technology of China
Chengdu, China
6 h-index
15 publications
1.1K citations
Tsinghua University
Beijing, China
6 h-index
8 publications
700 citations
Tianjin University
Tianjin, China
5 h-index
15 publications
1.7K citations

Chongqing University
Chongqing, China
5 h-index
13 publications
1.1K citations

Huazhong University of Science and Technology
Wuhan, China
5 h-index
14 publications
914 citations
1–8 of 276
18 publications · 1.1K citations
16
h-index
10 publications · 753 citations
10
h-index
8 publications · 681 citations
8
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Repository · 9 publications · 276 citations
8
h-index
8 publications · 830 citations
8
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