A Localized Primal-Dual Method for Centralized/Decentralized Federated Learning Robust to Data Heterogeneity
Author:
Affiliation:
1. NTT Social Information Laboratories, NTT Corporation, Tokyo, Japan
2. NTT DATA Mathematical Systems Inc., Tokyo, Japan
3. Kyoto University / OIST, Kyoto, Japan
4. NTT, Communication Science Laboratories, NTT Corporation, Tokyo, Japan
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Computer Networks and Communications,Information Systems,Signal Processing
Link
http://xplorestaging.ieee.org/ielx7/6884276/10380708/10373878.pdf?arnumber=10373878
Reference33 articles.
1. Federated learning: Strategies for improving communication efficiency;Konenỳ,2016
2. Federated Learning
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4. Unsupervised Recurrent Federated Learning for Edge Popularity Prediction in Privacy-Preserving Mobile-Edge Computing Networks
5. Learning Rate Optimization for Federated Learning Exploiting Over-the-Air Computation
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