A Modified UDP for Federated Learning Packet Transmissions

TL;DR

This paper proposes a modified UDP protocol tailored for federated learning to enhance efficiency and reliability in model parameter transmission, demonstrated through initial NS3 simulations with promising results for future large-scale deployment.

Contribution

A novel UDP modification specifically designed for federated learning environments, validated through simulation, with plans for further optimization and larger system testing.

Findings

Protocol shows promise in initial simulations

Potential for improved efficiency and reliability

Future work includes larger system testing and optimization

Abstract

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.