Mobility-aware and Privacy-protecting QoS Optimization in Mobile Edge Networks

With the rapid development of 5G technologies, the demand of quality of service (QoS) from edge users, including high bandwidth and low latency, has increased dramatically. QoS within a mobile edge network is highly dependent on the allocation of edge users. However, the complexity of user movement greatly challenges edge user allocation, leading to privacy leakage. In addition, updating massive data constantly in a dynamic mobile edge network also crucial to ensure efficiency. To address these challenges, this paper proposes a dynamic QoS optimization strategy ( MENIFLD_QoS ) in mobile edge networks based on incremental learning and federated learning. MENIFLD_QoS optimizes service cache in edge regions and allocates edge servers to edge users according to the locations of edge servers accessed by edge users in mobile scenarios. While optimizing regional service quality, the system can effectively protect user privacy. In addition, for dynamic incremental data, MENIFLD_QoS trains updated data based on the strategy of incremental learning hence significantly improves optimization speed. Experimental results on an edge QoS dataset show that the proposed strategy achieves global optimization in both multi-variable and multi-peak user allocation scenarios and notably enhances the training efficiency of the regional invocation model.