Clustered Federated Multi-Task Learning with Non-IID Data-Reference-Cited by-同舟云学术

Clustered Federated Multi-Task Learning with Non-IID Data

Published:2021-12 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)
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Author:

Xiao Yao¹,Shu Jiangang²,Jia Xiaohua³,Huang Hejiao¹

Affiliation:

1. School of Computer Science and Technology, Harbin Institute of Technology,Shenzhen,China

2. Peng Cheng National Laboratory,Department of New Networks,Shenzhen,China

3. City University of Hong Kong,Departmeut of Computer Science,Hong Kong, China

Funder

Natural Science Foundation of China

Key-Area Research and Development Program of Guangdong Province

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9763565/9763668/09763764.pdf?arnumber=9763764

Reference27 articles.

1. Overcoming Forgetting in Federated Learning on Non-IID Data;shoham;Proc of FL-NeurIPS'19,0

2. Federated learning with hierarchical clustering of local updates to improve training on nonIID data;briggs;Proc of International Joint Conference on Neural Networks,0

3. Communication-efficient distributed dual coordinate ascent;jaggi;Proc of the 27th NIPS,0

4. Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization Under Privacy Constraints

5. Robust Federated Learning in a Heterogeneous Environment;ghosh;ArXiv Preprint,2019

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3. Communication Efficiency and Non-Independent and Identically Distributed Data Challenge in Federated Learning: A Systematic Mapping Study;Applied Sciences;2024-03-24

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5. Clustered Federated Multitask Learning on Non-IID Data With Enhanced Privacy;IEEE Internet of Things Journal;2023-02-15