# Node Screening Method Based on Federated Learning with IoT in Opportunistic Social Networks
###### Abstract  
With the advent of the 5G era, the number of Internet of Things (IoT) devices has surged, and the population's demand for information and bandwidth is increasing. The mobile device networks in IoT can be regarded as independent \"social nodes\", and a large number of social nodes are combined to form a new \"opportunistic social network\". In this network, a large amount of data will be transmitted and the efficiency of data transmission is low. At the same time, the existence of \"malicious nodes\" in the opportunistic social network will cause problems of unstable data transmission and leakage of user privacy. In the information society, these problems will have a great impact on data transmission and data security; therefore, in order to solve the above problems, this paper first divides the nodes into \"community divisions\", and then proposes a more effective node selection algorithm, i.e., the FL node selection algorithm based on Distributed Proximal Policy Optimization in IoT (FABD) algorithm, based on Federated Learning (FL). The algorithm is mainly divided into two processes: multi-threaded interaction and a global network update. The device node selection problem in federated learning is constructed as a Markov decision process. It takes into account the training quality and efficiency of heterogeneous nodes and optimizes it according to the distributed near-end strategy. At the same time, malicious nodes are screened to ensure the reliability of data, prevent data loss, and alleviate the problem of user privacy leakage. Through experimental simulation, compared with other algorithms, the FABD algorithm has a higher delivery rate and lower data transmission delay and significantly improves the reliability of data transmission. opportunistic social network; federated learning; community restructuring; Internet of Things; deep reinforcement learning; mobile edge computing +
Footnote †: journal: Medical Computing  
realize the interconnection of the human, machine, and things at any time and place [2; 3; 4]. Because 5G technology has high-speed data transmission efficiency and a long transmission distance, in actual operation, it can effectively improve the original cellular data module [5], and it can also enrich the communication mode of the terminal and broaden the network frequency band. Therefore, on a 5G network, the coverage area of the IoT can be expanded, and the IoT has found huge development opportunities in the era of 5G networks. With the advent of the 5G era, the population's mobile data traffic is increasing rapidly, and the demand for network bandwidth and information is also increasing [6]. There are more and more types and numbers of devices in the IoT [7; 8; 9; 10]. Opportunistic network is a self-organizing network in which a complete path does not need to exist between the source node and the destination node, and the communication between the networks can be carried out through encounters in the process of node movement. In this network, two nodes exchange data in the way of \"storage-carry-forward\" to realize communication between nodes. Individual members in social networks have a relatively stable relationship system due to interaction, and interaction will also affect social behavior. The opportunistic social network combines the characteristics of the above two networks. With the rapid transmission of large-capacity data in the 5G network, the efficiency and reliability of data transmission at the edge are difficult to guarantee. Therefore, it is necessary to find a routing algorithm that can improve the efficiency of data transmission and ensure data reliability. Many researchers found that suitable node selection algorithms can effectively improve the delivery rate of data transmission and reduce the transmission delay. However, due to a lack of experience in dealing with complex social relations in opportunistic social networks, they often have difficulty in proposing effective node selection schemes.