Dual-personalized federated fast convergence model for efficient service recommendation

Abstract

Service recommendation in edge Service-Oriented Architecture (SOA) environments must balance privacy protection, recommendation accuracy, and the limited computing resources of user devices. Many existing federated recommendation methods protect raw interaction data, but often still rely on broad client participation, complex scoring models, or user-feature uploads for client grouping. This paper proposes a Dual-Personalized Federated Fast Convergence Model (DPFedFast) for efficient service recommendation. Compared with FedFast, DPFedFast introduces three technical improvements: first, clients are sampled according to the similarity of locally trained rating-function parameters rather than explicit user information; second, the recommendation model is simplified by adopting a lightweight single-layer neural rating function to reduce edge-side computation and communication; and third, a dual personalization mechanism separately updates personalized rating-function parameters and item embeddings to preserve user-specific preferences under the lightweight design. Extensive experiments conducted on four real-world datasets demonstrate the effectiveness of DPFedFast.