Federated Learning Over Wireless IoT Networks With Optimized Communication and Resources
Author:
Affiliation:
1. School of Electrical Engineering and Computer Science, Royal Institute of Technology, Stockholm, Sweden
2. Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, USA
Funder
ERA-NET Smart Energy Systems SG+ 2017 Program through “SMART-MLA” Project
FORMAS Project titled “Intelligent Energy Management in Smart Community with Distributed Machine Learning”
Swedish Research Council Project titled “Coding for Large-Scale Distributed Machine Learning”
Digital Futures Postdoc Fellowships
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing
Link
http://xplorestaging.ieee.org/ielx7/6488907/9866069/09712615.pdf?arnumber=9712615
Reference53 articles.
1. Energy Efficient Federated Learning Over Wireless Communication Networks
2. Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation
3. Efficient Federated Learning Algorithm for Resource Allocation in Wireless IoT Networks
4. Federated Learning with Downlink Device Selection
5. Optimal Online Data Partitioning for Geo-Distributed Machine Learning in Edge of Wireless Networks
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