FedPOIRec: Privacy Preserving Federated POI Recommendation with Social Influence
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by
Vasileios Perifanis, George Drosatos, Giorgos Stamatelatos, Pavlos S. Efraimidis
2021
Abstract
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.
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