Improving Communication Systems Through Federated Learning: An Optimization of Decision-Making-Reference-Cited by-同舟云学术

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Improving Communication Systems Through Federated Learning: An Optimization of Decision-Making

Published:2023-09-27 Issue: Volume: Page:
ISSN:
Container-title:2023 6th World Symposium on Communication Engineering (WSCE)
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Author:

Stergiou Konstantinos D.¹,Ishibashi Yutaka²,Goudos Sotirios K.³,Lagkas Thomas⁴,Sarigiannidis Panagiotis⁵,Psannis Konstantinos E.¹

Affiliation:

1. University of Macedonia,Department of Applied Informatics,Thessaloniki,Greece

2. Graduate School of Engineering, Nagoya Institute of Technology,Nagoya,Japan

3. Aristotle University of Thessaloniki,Department of Physics,Thessaloniki,Greece

4. International Hellenic University,Department of Computer Science,Kavala,Greece

5. University of Western Macedonia,Department of Informatics and Telecommunications Engineering,Kozani,Greece

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10365708/10365732/10365857.pdf?arnumber=10365857

Reference10 articles.

1. Federated Learning Approach Decouples Clients From Training a Local Model and With the Communication With the Server

2. A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison

3. Federated Learning Algorithms: Towards Next Generation Communication Systems

4. Optimising Decision Making on Communication Systems: The Federated Learning Approach

5. The role of communication systems in smart grids: Architectures, technical solutions and research challenges

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