Cooperative Edge Caching: Federated Deep Reinforcement Learning for IOT

On order to handle huge amounts of content on mobile networks and support the quickly expanding Internet of Thing and apps, edge buffering is a newly developed method. Nevertheless, the majority of optimization-based techniques used today are incapable of self-adapting in dynamic contexts. Present-day learning-based techniques are often put out in an organized way to address these issues. On the other hand, throughout the education and data transfer manipulate, network resources could be overused. We provide an integrated Edge cache system based on Merged Deep neural networks to tackle the intricate and ever-changing control problems. By using the BSs' first-round training settings as the starting input for local training while uploading nearly-optimal local variables to them so they may take part in the subsequent round of global education, FADE allows base stations to collaboratively develop a shared predictive model. Additionally, we demonstrate that the suggested FADE converges and reaches the expected converging. The rapid proliferation of Internet of Things (IoT) devices necessitates innovative solutions for managing the vast amounts of data generated and optimizing network resources. This research explores the integration of Federated Deep Reinforcement Learning (FDRL) with decentralized cooperative edge caching to enhance IoT network efficiency. FDRL allows IoT devices to collaboratively learn optimal caching strategies while maintaining data privacy, as model updates rather than raw data are shared among devices. Decentralized cooperative edge caching leverages the distributed nature of edge devices to store and retrieve data efficiently, reducing latency and bandwidth usage. This study proposes a novel FDRL framework that dynamically adjusts caching policies based on real-time network conditions and user demands. The framework's effectiveness is evaluated through extensive simulations, demonstrating significant improvements in cache hit rates, network latency, and overall system throughput. Additionally, the decentralized nature of the approach enhances robustness against network failures and security breaches.