Device Selection for Resource-Efficient Edge Caching in a Federated Learning Framework

Edge caching enhances user experience and network efficiency by locally storing popular content. Using federated learning to find popular content enables model training directly on edge devices, eliminating the need to share raw content request data. However, involving multiple devices in training can be resource-intensive. This paper proposes a device selection method to enhance edge caching performance by accurately predicting content popularity while minimizing resource consumption. Experiments on the MovieLens 1M dataset indicate that 95.23% of achievable cache efficiency can be obtained with 70% of devices. Comparison with the existing device selection methods demonstrates the improved state-of-the-art performance of the proposed approach.