Optimized Transfer Learning: Application for Wireless Channel Selection

Abstract

Recently, transfer learning (TL) has emerged as a powerful machine learning method in distributed environments. Transferring the knowledge between distributed agents helps reduce both learning time and computing costs. However, in a communication system, the advantage of TL comes with communication costs. To make an optimal decision of transfer between two agents, we try to answer three key questions: i) which information should be transferred from a source to a target?, ii) how this transferred information will be adapted to the target? and iii) when should TL be triggered to optimize the costs?. To this end, we introduce a new concept of similarity based on the Best Approximation Theory and a general transfer rule. Then, we propose a model to evaluate the feasibility and optimality of TL. We verify our proposed model in the context of the wireless channel selection problem using contextual multi-armed bandits. Experimental results show optimal TL decisions can be made, and Extra Action is an efficient technique for TL in channel selection.