Aggregation using Genetic Algorithms for Federated Learning in Industrial Cyber-Physical Systems

Industry and academics are interested in industrial cyber-physical systems (ICPS). Complexity makes it hard to grasp these systems design and functioning. By offering FedGA-ICPS, a federated learning framework based on genetic algorithms, we can solve ICPSś performance and decision support. To simulate the structure and behavior of these systems, we use ICPS. FedGA-ICPS investigates the performance of the ICPS sensors by offering locally integrated learning models. Genetic algorithm then speeds up and improves federated learning aggregation. Transfer learning is used to disseminate model parameters across restricted entities. Fashion MNIST’s initiative achieved notable outcomes.