Effective 3C Resource Utilization and Fair Allocation Strategy for Multi-Task Federated Learning
Author:
Affiliation:
1. School of Information and Electronic Engineering, Advanced Institute of Industrial Technology, Shinagawa, Tokyo, Japan
2. Department of Sciences and Informatics, Muroran Institute of Technology, Muroran, Japan
Funder
JSPS KAKENHI
Leading Initiative for Excellent Young Researchers (LEADER), MEXT, Japan
JST, PRESTO, Japan
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/9882533/9895461/10147854.pdf?arnumber=10147854
Reference34 articles.
1. Energy-Efficient Massive MIMO for Federated Learning: Transmission Designs and Resource Allocations
2. No Free Lunch Theorem for Security and Utility in Federated Learning
3. Fine-Grained Management in 5G: DQL Based Intelligent Resource Allocation for Network Function Virtualization in C-RAN
4. Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation
5. Optimizing Federated Learning on Non-IID Data with Reinforcement Learning
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