Personalized Computation Offloading and Service Caching for Mobile Edge Computing in Heterogeneous Networks

With the rapid advancement of Internet of Things (IoT), there is a growing demand for intelligent applications with varying requirements (e.g. delay, reliability, and energy). Mobile Edge Computing (MEC) can enhance the responsiveness of these applications by caching specific computing services on edge servers. However, the efficiency of task offloading and service caching at MEC servers is often hindered by the diversity of user preferences and privacy across different edge node service regions, especially in heterogeneous networks. To this end, we introduce a personalized computation offloading and service caching method that integrates Deep Reinforcement Learning (DRL) with Personalized Federated Learning (PFL), termed DPFL, aimed at optimizing computation performances in a distributed and privacy-preserving manner. The DPFL employs DRL to jointly optimize computation offloading and service caching placement, reducing task latency and energy consumption. Simultaneously, it leverages personalized federated learning to develop local service prediction models, offering tailored service caching policies for users in heterogeneous regions while safeguarding data privacy. Simulation results demonstrate that our algorithm surpasses existing policies in reducing application delay, energy consumption, and improving system cache hit ratio.