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With the growing number of Location-Based Social Networks, privacy preservinglocation prediction has become a primary task for helping users discover newpoints-of-interest (POIs). Traditional systems consider a centralized approachthat requires the transmission and collection of users' private data. In thiswork, we present FedPOIRec, a privacy preserving federated learning approachenhanced with features from users' social circles for top-$N$ POIrecommendations. First, the FedPOIRec framework is built on the principle thatlocal data never leave the owner's device, while the local updates are blindlyaggregated by a parameter server. Second, the local recommenders getpersonalized by allowing users to exchange their learned parameters, enablingknowledge transfer among friends. To this end, we propose a privacy preservingprotocol for integrating the preferences of a user's friends after thefederated computation, by exploiting the properties of the CKKS fullyhomomorphic encryption scheme. To evaluate FedPOIRec, we apply our approachinto five real-world datasets using two recommendation models. Extensiveexperiments demonstrate that FedPOIRec achieves comparable recommendationquality to centralized approaches, while the social integration protocol incurslow computation and communication overhead on the user side.

FedPOIRec: Privacy Preserving Federated POI Recommendation with Social Influence