A novel federated learning aggregation algorithm for AIoT intrusion detection

Nowadays, the development of Artificial Intelligence of Things (AIoT) is advancing rapidly, and intelligent devices are increasingly exposed to more security risks on the network. Deep learning-based intrusion detection is an effective security defence approach. Federated learning (FL) is capable of enabling deep learning models to be trained on local clients without uploading their data to a central server. This paper proposes a novel federated learning aggregation algorithm called fed-dynamic gravitational search algorithm (Fed-DGSA), which incorporates the GSA algorithm to optimize the weight updating process of FL local models. During the updating process, the decay rate of the gravity coefficient is optimized and random perturbations and dynamic weights are introduced to ensure a more stable and efficient FL aggregation process. The experimental results show that the detection accuracy of Fed-DGSA has reached about 97.8%, and it is demonstrated that the model trained using Fed-DGSA achieves higher accuracy compared to Fed-Avg. This paper proposes a new federated learning aggregation algorithm called Fed-dynamic gravitational search algorithm (Fed-DGSA). This algorithm enhances the aggregation efficiency of the federated learning global model and, through simulations, it has been verified that it improves the detection accuracy of the model. image