Slimmable Federated Reinforcement Learning for Energy-Efficient Proactive Caching

Recent advances in deep learning have successfully replaced classical algorithms with machine learning models based on neural networks (NNs). This is particularly prevalent in proactive caching. As NNs grows more capable as their size in terms of storage and computation increases, NN-based proactive caching achieves performance improvement. Nonetheless, there remain challenges in implementing NN-based proactive caching in realistic environments with dynamic user movement. These are due to the fixed structure of NNs that should expand the input size to match the dimensions of the input with the dimensions of the NN’s input units. To address these challenges, this paper proposes a scalable proactive caching framework, named slimmable federated reinforcement learning (SlimFRL). By adopting slimmable neural networks (SNNs) in FRL, our SlimFRL easily adjusts the widths of the SNNs during training according to the number of users. Moreover, due to the scalability of SNNs, our SlimFRL can set the appropriate input dimension while not using imputation, leading to performance improvement. This paper also validates the performance and advantages of SlimFRL in terms of reward and additional cost functions. Additionally, this paper proposes several training algorithms for SlimFRL and corroborates their superiority with convergence analysis and various experiments.