FedCLS: Class-Aware Federated Learning in a Heterogeneous Environment
Author:
Affiliation:
1. Department of Electrical and Electronic Engineering, Hanyang University, Ansan, South Korea
Funder
Brain Pool program funded by Ministry of Science and ICT through the National Research Foundation of Korea
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Networks and Communications
Link
http://xplorestaging.ieee.org/ielx7/4275028/10168539/10130085.pdf?arnumber=10130085
Reference46 articles.
1. HFDRL: An Intelligent Dynamic Cooperate Cashing Method Based on Hierarchical Federated Deep Reinforcement Learning in Edge-Enabled IoT
2. Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management
3. Soft Cooperative Spectrum Sensing Performance Under Imperfect and Non Identical Reporting Channels
4. A Performance Efficient Approach of Global Training in Federated Learning
5. Realizing the Heterogeneity: A Self-Organized Federated Learning Framework for IoT
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1. DRL-empowered joint batch size and weighted aggregation adjustment mechanism for federated learning on non-IID data;ICT Express;2024-08
2. Enhancing IoT Healthcare with Federated Learning and Variational Autoencoder;Sensors;2024-06-04
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