Optimizing Data Transfer and Convergence Time for Federated Learning based on NSGA II

Face recognition from digital images is used for surveillance and authentication in cities, organizations, and personal devices. internet of things (iot)-powered face recognition systems use multiple sensors and one or more servers to process data. all sensor data from initial methods was sent to the central server for processing, raising concerns about sensitive data disclosure. the main concern was that all data from all sectors that could contain confidential information was placed in a central server. federated learning can solve this problem by using several local model training servers for each region and a central aggregation server to form a global model in iot networks. this article presents a novel approach to optimize data transfer and convergence time in federated learning for a face recognition task using non-dominated sorting genetic algorithm ii (nsga ii). the aim of the study is to balance the trade-off between training time and model accuracy in a federated learning environment. the results demonstrate the effectiveness of the proposed approach in reducing data transfer and convergence time, leading to improved performance in face recognition accuracy. this research provides insights for researchers and practitioners to enhance the efficiency of federated learning in real-world applications.