Abstract
With the rapid proliferation of collaborative mobile applications, edge computing has emerged as a promising paradigm to deliver ultra-low latency and localized services. However, conventional Edge Server Placement (ESP) strategies primarily optimize physical transmission delay and server load balancing, largely overlooking the intense cross-server communication overhead induced by user social interactions. Furthermore, directly utilizing fine-grained user trajectories and social graphs to guide placement poses severe privacy risks. In this paper, we propose a socially aware edge server placement framework, termed Fed-STSR, where the primary contribution is a multi-objective optimization formulation that explicitly integrates user social relationships alongside network latency, service migration, and server load balancing. To enable this framework without compromising user confidentiality, federated representation learning coupled with a calibrated Laplace differential privacy mechanism serves as an enabling layer, compressing sensitive spatiotemporal patterns into bounded representations locally. The resulting discrete placement problem is solved using an enhanced Trees Social Relations (TSR) algorithm operating over sparse adjacency structures. Extensive trace-driven simulations based on the Gowalla and Brightkite datasets mapped onto real urban base station layouts demonstrate the effectiveness of the proposed approach. Compared to state-of-the-art baselines, Fed-STSR reduces average service latency by up to 31.5% and achieves superior server load balance while maintaining rigorous user-level privacy guarantees.