Automated Feature Selection with Local Gradient Trajectory in Split Learning
Selim İckin · 2023
In telecommunication networks, many factors that influence QoE are inherently distributed in the network; related to creation, delivery, and presentation of the content at the end-user terminal. Split Learning (SL) is a scalable distributed machine learning (ML) technique that enables joint training and inference on decentralized datasets. These decentralized datasets can be potentially large and sensitive, and can not be collected at a central server. SL allows decentralized models to be trained at where the corresponding local data are collected. Still, the computation cost of SL at the decentralized nodes can be high, negatively impacting the training time and energy consumption, especially when there are too many local decentralized ML input attributes. In this paper, we present a Gradient trajectory based local feature selection (GS) technique that deposes noncontributing input features from local nodes early in a supervised SL training, where those nodes do not have access to the target label, e.g., QoE metric. With the proposed approach, more than 50% reduction in memory and in average more than 20% reduction in computation complexity were observed over an empirical study.