An Analysis of the Impact of Network Conditions on Edge Federated Learning

Edge Federated learning represents a novel architecture at the intersection of federated learning, edge computing, and cloud computing. Edge Federated learning enables efficient and scalable machine learning model training at the network edge by preserving local training data privacy. The performance of this architecture is measured in terms of model accuracy, training time, and communication cost, which vary with the quality and availability of the underlying hosted network infrastructure and computing resources. This paper addresses the challenges related to network infrastructure and systematically characterizes network dynamics such as latency, packet loss, and bandwidth constraints in edge-federated learning environments and quantifies their impact on model training time and accuracy. Furthermore, this paper establishes linear regression equations to predict training time, using regression analysis on quantified data to inform the model. The results showed that high latency, low bandwidth, and high packet drop do not affect accuracy. Additionally, high latency and packet drop have a significant impact on model training time, whereas low bandwidth has a minimal impact.