Federated Learning With Non-IID Data: A Survey-Reference-Cited by-同舟云学术

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Federated Learning With Non-IID Data: A Survey

Published:2024-06-01 Issue:11 Volume:11 Page:19188-19209
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Lu Zili¹^ORCID,Pan Heng¹,Dai Yueyue²^ORCID,Si Xueming¹,Zhang Yan³^ORCID

Affiliation:

1. Frontier Information Technology Research Institute, Zhongyuan University of Technology, Zhengzhou, China

2. Research Center of 6G Mobile Communications, School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

3. Department of Informatics, University of Oslo, Oslo, Norway

Funder

National Natural Science Foundation of China

Pre-Research Project of Songshan Laboratory

Key Research Project for Higher Education of Henan Province

EU Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie Grant

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6488907/10536937/10468591.pdf?arnumber=10468591

Reference117 articles.

1. Deep Reinforcement Learning for Resource Allocation in Blockchain-Based Federated Learning

2. Federated Learning for Internet of Things: A Comprehensive Survey

3. Communication-efficient learning of deep networks from decentralized data;McMahan

4. Byzantine-Robust and Communication-Efficient Distributed Non-Convex Learning Over Non-IID Data

5. Optimizing Federated Learning on Non-IID Data with Reinforcement Learning

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1. Mobility-aware federated self-supervised learning in vehicular network;Urban Lifeline;2024-09-11

2. Distributed Hierarchical Temporal Graph Learning for Communication-Efficient High-Dimensional Industrial IoT Modeling;IEEE Internet of Things Journal;2024-09-01

3. Efficient Inter-Datacenter AllReduce With Multiple Trees;IEEE Transactions on Network Science and Engineering;2024-09

4. A Systematic Literature Review on the Use of Federated Learning and Bioinspired Computing;Electronics;2024-08-10

5. Federated Meta-Learning: Methodologies and Directions;Lecture Notes in Computer Science;2024

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