Privacy-aware service placement for mobile edge computing via federated learning

Mobile edge clouds can offer delay-sensitive services to users by deploying storage and computing resources at the network edge. Considering resource-limited edge server, it is impossible to deploy all services on the edge clouds. Thus, many existing works have addressed the problem of arranging services on mobile edge clouds for better quality of services (QoS) to users. In terms of existing service placement strategies, the historical data of requesting services by users are collected to analyze. However, those historical data belong to users' sensitive information, without appropriate privacy preserving measures may hinder the implementation of traditional service arrangement strategies. Service placement with considering users' privacy and limited resources of mobile edge clouds, is an extremely urgent problem to be solved. In this paper, we propose a privacy-aware service placement (PSP) scheme to address the problem of service placement with privacy-awareness in the edge cloud system. The purpose of PSP mechanism is to protect users' privacy and provide better QoS to users when obtaining services from mobile edge clouds. Specifically, whether service placement on mobile edge clouds or not is modeled as a 0-1 problem. Then, a hybrid service placement algorithm is proposed that combines a centralized greedy algorithm and distributed federated learning. Compared with other optimization schemes, the simulation experiments show that PSP algorithm could not only protect users' privacy but also meet users' service demands through mobile edge clouds. (C) 2019 Elsevier Inc. All rights reserved.