Age-Based Federated Learning Approach to In-Network Caching: An Online Scheduling Policy

We develop an accurate real-time scheduling framework for federated learning (FL) in wireless caching networks to guarantee the successful delivery of files at a low cost and with a short delay. The following persisting challenges motivated our work: i) Enforcing an excessive number of FL model update per communication round is infeasible due to the limited backhaul spectrum; ii) Naive scheduling policy accounting for FL model update renders service backlogs, thus leading to network parameter staleness and in-network caching utility (ICU) deterioration. Optimal scheduling in FL is challenging, as the mobile users' preferences for content, request patterns, and network traffic are dynamic and unknown. To tackle that challenge, we first formulate an instantaneous ICU optimization problem against the stale FL models. Afterward, based on the concept of age-of-update (AoU), we propose a federated learning with an unsatisfactory set selection (FedUSS) approach capable of executing the multiple-tasks of short-term predictions and making cache replacement decisions at low cost. Theoretical and numerical analyses manifest the effectiveness of our approach.