A Modified UDP for Federated Learning Packet Transmissions
This is an incremental improvement for Federated Learning systems, addressing network efficiency and reliability in distributed model training.
This paper tackles the problem of ensuring efficiency and reliability in model parameter transport for Federated Learning by introducing a Modified User Datagram Protocol (UDP), with preliminary results providing confidence in its capabilities for future testing on larger systems.
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.