Federated learning based intelligent edge computing technique for video surveillance

With the explosion of global data,centralized cloud computing cannot provide low-latency,high-efficiency video surveillance services.A distributed edge computing model was proposed,which directly processed video data at the edge node to reduce the transmission pressure of the network,eased the computational burden of the central cloud server,and reduced the processing delay of the video surveillance system.Combined with the federated learning algorithm,a lightweight neural network was used,which trained in different scenarios and deployed on edge devices with limited computing power.Experimental results show that,compared with the general neural network model,the detection accuracy of the proposed method is improved by 18%,and the model training time is reduced.