Adaptive Clustering Based Personalized Federated Learning Framework for Next POI Recommendation With Location Noise

Next point-of-interest (POI) recommendation has been a hot research topic, which enables new paradigms for kinds of location-based services in real-world scenarios. Due to the privacy concerns and rigorous data regulations, federated learning provides a distributed learning framework to collaboratively train the recommendation model without sharing the highly sensitive POI data with others. However, there exist two main challenges, namely
location noise
, and
balance between personalization and knowledge sharing
, seriously restrict the development of the federated next POI recommendation. To this end, in this work, we propose an adaptive clustering based personalized federated learning framework for next POI recommendation with location noise, named
CPF-POI
, to address the above challenges. In detail, within the local client, a location recovery module can efficiently remove noises under the given assumption from the noisy POI data in which the recovery error bound can be theoretically proved. Then, within the parameter server, an adaptive clustering scheme is proposed to capture the internal relatedness among all clients to augment positive knowledge sharing. In order to make a balance between personalization and knowledge sharing under personalized federated learning framework, we design an alternative optimization process between clustering similar clients and minimizing local personalized loss functions. Finally, extensive experiments are conducted on two diverse real-world datasets to show the advantages of
CPF-POI
over state-of-the-art methods.