  The federated recommendation system is an emerging AI service architecture
that provides recommendation services in a privacy-preserving manner. Using
user-relation graphs to enhance federated recommendations is a promising topic.
However, it is still an open challenge to construct the user-relation graph
while preserving data locality-based privacy protection in federated settings.
Inspired by a simple motivation, similar users share a similar vision
(embeddings) to the same item set, this paper proposes a novel Graph-guided
Personalization for Federated Recommendation (GPFedRec). The proposed method
constructs a user-relation graph from user-specific personalized item
embeddings at the server without accessing the users' interaction records. The
personalized item embedding is locally fine-tuned on each device, and then a
user-relation graph will be constructed by measuring the similarity among
client-specific item embeddings. Without accessing users' historical
interactions, we embody the data locality-based privacy protection of vanilla
federated learning. Furthermore, a graph-guided aggregation mechanism is
designed to leverage the user-relation graph and federated optimization
framework simultaneously. Extensive experiments on five benchmark datasets
demonstrate GPFedRec's superior performance. The in-depth study validates that
GPFedRec can generally improve existing federated recommendation methods as a
plugin while keeping user privacy safe. Code is available to ease
reproducibility
