A Modified UDP for Federated Learning Packet Transmissions
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Abstract (arXiv)
This paper introduces a Modified User Datagram Protocol (UDP) for Federated Learning to ensure efficiency and reliability in the model parameter transport process, maximizing the potential of the Global model in each Federated Learning round. In developing and testing this protocol, the NS3 simulator is utilized to simulate the packet transport over the network and Google TensorFlow is used to create a custom Federated learning environment. In this preliminary implementation, the simulation contains three nodes where two nodes are client nodes, and one is a server node. The results obtained in this paper provide confidence in the capabilities of the protocol in the future of Federated Learning therefore, in future the Modified UDP will be tested on a larger Federated learning system with a TensorFlow model containing more parameters and a comparison between the traditional UDP protocol and the Modified UDP protocol will be simulated. Optimization of the Modified UDP will also be explored to improve efficiency while ensuring reliability.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 13 references that are in the map. First posted on arXiv 11 Aug 2022.
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References · 8
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
- 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
- 2018Federated Learning for Mobile Keyboard Predictioncited by 572
- 2020Federated Learning with Matched Averagingcited by 564
- 2019Federated Machine Learning: Concept and Applicationscited by 412
- 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
- 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
- 2021Decentralized Federated Averagingcited by 114
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