Improving Federated Learning With Quality-Aware User Incentive and Auto-Weighted Model Aggregation-Reference-Cited by-同舟云学术

Improving Federated Learning With Quality-Aware User Incentive and Auto-Weighted Model Aggregation

Author:

Deng Yongheng¹^ORCID,Lyu Feng²^ORCID,Ren Ju¹^ORCID,Chen Yi-Chao³,Yang Peng⁴^ORCID,Zhou Yuezhi¹^ORCID,Zhang Yaoxue¹

Affiliation:

1. Department of Computer Science and Technology, BNRist, Tsinghua University, Beijing, China

2. School of Computer Science and Engineering, Central South University, Changsha, China

3. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

4. School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Guoqiang Institute, Tsinghua University

Young Elite Scientists Sponsorship Program

Key-Area Research and Development Program of Guangdong Province

Natural Science Foundation of Hunan Province

Key R&D Program of Hunan Province of China

Young Talents Plan of Hunan Province of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Hardware and Architecture,Signal Processing

Link

Reference48 articles.

4. On the convergence of fedavg on non-IID data;li,2019

5. SCAFFOLD: Stochastic controlled averaging for federated learning;karimireddy;Proc Int Conf Mach Learn,2020

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