FedPOIRec: Privacy Preserving Federated POI Recommendation with Social Influence
This addresses privacy concerns in location-based services for users of social networks, but it is incremental as it builds on existing federated learning and encryption techniques.
The paper tackles the problem of privacy-preserving point-of-interest (POI) recommendation by proposing FedPOIRec, a federated learning approach that integrates social influence without transmitting private data, achieving comparable recommendation quality to centralized methods with low overhead.
With the growing number of Location-Based Social Networks, privacy preserving location prediction has become a primary task for helping users discover new points-of-interest (POIs). Traditional systems consider a centralized approach that requires the transmission and collection of users' private data. In this work, we present FedPOIRec, a privacy preserving federated learning approach enhanced with features from users' social circles for top-$N$ POI recommendations. First, the FedPOIRec framework is built on the principle that local data never leave the owner's device, while the local updates are blindly aggregated by a parameter server. Second, the local recommenders get personalized by allowing users to exchange their learned parameters, enabling knowledge transfer among friends. To this end, we propose a privacy preserving protocol for integrating the preferences of a user's friends after the federated computation, by exploiting the properties of the CKKS fully homomorphic encryption scheme. To evaluate FedPOIRec, we apply our approach into five real-world datasets using two recommendation models. Extensive experiments demonstrate that FedPOIRec achieves comparable recommendation quality to centralized approaches, while the social integration protocol incurs low computation and communication overhead on the user side.