Intelligent hierarchical federated learning system based on semi-asynchronous and scheduled synchronous control strategies in satellite network

Abstract

Federated learning (FL) is a technology that allows multiple devices to collaboratively train a global model without sharing original data, which is a hot topic in distributed intelligent systems. Combined with satellite network, FL can overcome the geographical limitation and achieve broader applications. However, it also faces the issues such as straggler effect, unreliable network environments and non-independent and identically distributed (Non-IID) samples. To address these problems, we propose an intelligent hierarchical FL system based on semi-asynchronous and scheduled synchronous control strategies in cloud-edge-client structure for satellite network. Our intelligent system effectively handles multiple client requests by distributing the communication load of the central cloud to various edge clouds. Additionally, the cloud server selection algorithm and the edge-client semi-asynchronous control strategy minimize clients’ waiting time, improving the overall efficiency of the FL process. Furthermore, the center-edge scheduled synchronous control strategy ensures the timeliness of partial models. Based on the experiment results, our proposed intelligent hierarchical FL system demonstrates a distinct advantage in global accuracy over traditional FedAvg, achieving 2% higher global accuracy within the same time frame and reducing 52% training time to achieve the target accuracy.