Participant Selection for Federated Learning With Heterogeneous Data in Intelligent Transport System

Intelligent Transportation Systems (ITS) utilises the growing trend of both communication technologies and intelligent analytics to make transportation systems more smart and efficient. Federated Learning, a privacy-preserving machine learning paradigm shows promise in being applied in this field. However, the data and device heterogeneity, and highly dynamic environment in ITS pose challenges to the performance of federated learning. One of the recent approaches to address the challenges are to choose proper participants from available clients during training. However, this research field is not fully investigated yet, and many works are still based on the classic random-based selection scheme. In this paper, we present Newt, an enhanced federated learning approach. On one hand, it includes a new client selection utility that explores the trade-off between accuracy performance in each round and system progress. On the other hand, it highlights a feedback control on the selector. Specifically, we implement a control on the selection frequency as a new dimension of client selection method design. We evaluate the proposed system with DNN training tasks on large scale FEMNIST-based datasets that are of different heterogeneity properties. The experiments show that our method outperforms the other baseline methods by as large as 20%.

Automated summary

Intelligent Transportation Systems (ITS) utilize communication technologies and intelligent analytics to enhance transportation systems. Federated Learning, a privacy-preserving machine learning paradigm, holds promise in this field. However, the heterogeneity of data and devices in ITS, as well as the dynamic environment, present challenges for federated learning. One approach to address these challenges is to select participants properly during training. This paper introduces Newt, an enhanced federated learning approach that considers accuracy performance and system progress during client selection, and includes a feedback control mechanism. Experimental results demonstrate a significant performance improvement of up to 20% compared to other methods.