FedNN: Federated learning on concept drift data using weight and adaptive group normalizations

Author:

Kang MyeongkyunORCID,Kim Soopil,Jin Kyong Hwan,Adeli Ehsan,Pohl Kilian M.ORCID,Park Sang HyunORCID

Funder

National Research Foundation of Korea

Daegu-Gyeongbuk Institute of Science & Technology

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference44 articles.

1. Federated machine learning: Concept and applications;Yang;ACM Trans. Intell. Syst. Technol.,2019

2. Federating recommendations using differentially private prototypes;Ribero;Pattern Recognit.,2022

3. Communication-efficient learning of deep networks from decentralized data;McMahan,2017

4. Efficient federated multi-view learning;Huang;Pattern Recognit.,2022

5. X. Li, K. Huang, W. Yang, S. Wang, Z. Zhang, On the convergence of fedavg on non-IID data, in: International Conference on Learning Representations, 2019.

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