Low-Latency Federated Learning With DNN Partition in Distributed Industrial IoT Networks
Author:
Affiliation:
1. School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China
2. Data61, CSIRO, Sydney, NSW, Australia
3. Department of Electronics Engineering, Shanghai Jiao Tong University, Shanghai, China
Funder
National Key Project
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Future Network Grant of Provincial Education Board in Jiangsu
Zhejiang Laboratory Open Research Project
Jiangsu Specially-Appointed Professor Program 2021
Shanghai Kewei
Pudong
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Networks and Communications
Link
http://xplorestaging.ieee.org/ielx7/49/10044995/09999679.pdf?arnumber=9999679
Reference51 articles.
1. UAV Communications for Sustainable Federated Learning
2. Matching-Theory-Based Low-Latency Scheme for Multitask Federated Learning in MEC Networks
3. Jointly Optimizing Client Selection and Resource Management in Wireless Federated Learning for Internet of Things
4. Energy-Efficient Federated Learning Over UAV-Enabled Wireless Powered Communications
5. Joint User Association and Resource Allocation for Wireless Hierarchical Federated Learning With IID and Non-IID Data
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