Recently, smart cities, smart homes, and smart medical systems have challenged the functionality and connectivity of the large-scale Internet of Things (IoT) devices. Thus, with the idea of offloading intensive computing tasks from them to edge nodes (ENs), edge computing emerged to supplement these limited devices. Benefit from this advantage, IoT devices can save more energy and still maintain the quality of the services they should provide. However, computational offload decisions involve federation and complex resource management and should be determined in the real-time face to dynamic workloads and radio environments. Therefore, in this work, we use multiple deep reinforcement learning (DRL) agents deployed on multiple edge nodes to indicate the decisions of the IoT devices. On the other hand, with the aim of making DRL-based decisions feasible and further reducing the transmission costs between the IoT devices and edge nodes, federated learning (FL) is used to train DRL agents in a distributed fashion. The experimental results demonstrate the effectiveness of the decision scheme and federated learning in the dynamic IoT system.INDEX TERMS Federated learning, computation offloading, IoT, edge computing.
Because of the exponential growth of mobile users' demand for multimedia services in recent years, the increasing network traffic load gets a close attention of the mobile network operators. For the mobile traffic explosion issue to be solved, there are many efforts trying to offload the mobile traffic from infrastructure cellular links to direct local short-range communications among groups of users, which is called device-to-device sharing (D2D) in mobile social networks.Although there have been a number of studies for improving the exploitation of friends, contents, and sharing performance, there is no any large-scale measurement-based study to analyze the realistic D2D sharing service. We focus on the empirical trace from Xender, a popular mobile application for D2D sharing, and implement an effective big data processing platform based on Spark with customized algorithms. Extensive analysis and discussions are carried out from the perspectives of general time series statistics, content properties, and social graph basics. The trace-driven analysis exploits a number of implications regarding power law distribution for content popularity disparity, clustering effects of user relationships, and so on. We further discuss the potentials of improving Xender's quality of service and optimizing its system resource, and hopefully, our study can offer useful guidelines for not only Xender but also those growing global social D2D sharing services.
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