FL-PERF: Predicting TCP Throughput with Federated Learning
Han Nay Aung, Hiroyuki Ohsaki · 2023
This paper addresses a research question: - how accurately can a TCP throughput prediction model be constructed while preserving the privacy of a large number of Internet users? In the field of communication networks, accurate performance prediction of TCP flows is crucial for realizing high-quality services. In recent years, machine learning techniques have advanced and approaches for TCP throughput prediction based on centralized machine learning have emerged. However, approaches for TCP throughput prediction lack the privacy protection of Internet users and struggle to cope with a large amount of training data. Federated Learning (FL) is a novel decentralized machine learning paradigm that was introduced in 2017, allowing for multiple learning clients to collaboratively train the parameters of the global model. In this paper, we propose the Federated Learning-based PERFormance predictor (FL-PERF) of TCP flows, which builds a global TCP throughput prediction model using FL with multiple learning clients in a privacy-preserving manner. Through experiments, we investigate the accuracy of the TCP throughput prediction model obtained with FL-PERF through experiments and then discuss its privacy and scalability.