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, namelylocation noise, andbalance 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, namedCPF-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 ofCPF-POIover state-of-the-art methods. improvement across all metrics on average.

With the raised privacy concerns and rigorous data regulations, federated learning has become a hot collaborative learning paradigm for the recommendation model without sharing the highly sensitive POI data. However, the time-sensitive, heterogeneous, and limited POI records seriously restrict the development of federated POI recommendation. To this end, in this paper, we design the fine-grained preference-aware personalized federated POI recommendation framework, namely PrefFedPOI, under extremely sparse historical trajectories to address the above challenges. In details, PrefFedPOI extracts the fine-grained preference of current time slot by combining historical recent preferences and periodic preferences within each local client. Due to the extreme lack of POI data in some time slots, a data amount aware selective strategy is designed for model parameters uploading. Moreover, a performance enhanced clustering mechanism with reinforcement learning is proposed to capture the preference relatedness among all clients to encourage the positive knowledge sharing. Furthermore, a clustering teacher network is designed for improving efficiency by clustering guidance. Extensive experiments are conducted on two diverse real-world datasets to demonstrate the effectiveness of proposed PrefFedPOI comparing with state-of-the-arts. In particular, personalized PrefFedPOI can achieve 7% accuracy improvement on average among data-sparsity clients.

The advancement of smart wearable devices and location-based smart services has enabled a new paradigm for smart human mobility prediction (HMP), which has a broad range of applications in smart healthcare and smart cities. Due to the privacy concerns and rigorous data regulations, federated learning provides a distributed learning framework to collaboratively train the HMP model without sharing the highly sensitive location data with others. However, in real-world scenarios, federated human mobility prediction suffers from data heterogeneity challenge, which includes two main aspects: heterogeneity mobility patterns, and data scarcity. In this paper, we propose an end-to-end federated representation learning framework for human mobility prediction, named FR-HMP, to overcome all the above obstacles. Specially, in order to enhance the representation abilities of data-scarcity clients, a two-phase learning process is proposed. The clustering module could cluster similar clients together on the parameter server to address the heterogeneous mobility patterns, and the representation learning module learns the enhanced representations of each client through the graph learning layer and graph convolution layer on the third-part server. Finally, extensive experiments are conducted using two diverse real-world HMP datasets to show the advantages of FR-HMP over state-of-the-art methods.