Achieving Optimal Cache Utility in Constrained Wireless Networks through Federated Learning

Edge computing allows constrained end devices in wireless networks to offioad heavy computing tasks or data storage when local resources are insufficient. Edge nodes can provide resources such as the bandwidth, storage and innetwork compute power. For example, edge nodes can provide data caches to which constrained end devices can off-load their data and from where user can access data more effectively. However, fair allocation of these resources to competing end devices and data classes while providing good Quality of Service is a challenging task, due to frequently changing network topology and/or traffic conditions. In this paper, we present Federated learning-based dynamic Cache allocation (FedCache) for edge caches in dynamic, constrained networks. FedCache uses federated learning to learn the benefit of a particular cache allocation with low communication overhead. Edge nodes learn locally to adapt to different network conditions and collaboratively share this knowledge so as to avoid having to transmit all data to a single location. Through this federated learning approach, nodes can find resource allocations that result in maximum fairness or efficiency in terms of the cache hit ratio for a given network state. Simulation results show that cache resource allocation using FedCache results in optimal fairness or efficiency of utility for different classes of data when compared to proportional allocation, while incurring low communication overhead.