FedGeo: Privacy-Preserving User Next Location Prediction with Federated
  Learning
Chung Park Taekyoon Choi Taesan Kim Mincheol Cho Junui Hong Minsung Choi Jaegul Choo
A User Next Location Prediction (UNLP) task, which predicts the next location
that a user will move to given his/her trajectory, is an indispensable task for
a wide range of applications. Previous studies using large-scale trajectory
datasets in a single server have achieved remarkable performance in UNLP task.
However, in real-world applications, legal and ethical issues have been raised
regarding privacy concerns leading to restrictions against sharing human
trajectory datasets to any other server. In response, Federated Learning (FL)
has emerged to address the personal privacy issue by collaboratively training
multiple clients (i.e., users) and then aggregating them. While previous
studies employed FL for UNLP, they are still unable to achieve reliable
performance because of the heterogeneity of clients' mobility. To tackle this
problem, we propose the Federated Learning for Geographic Information (FedGeo),
a FL framework specialized for UNLP, which alleviates the heterogeneity of
clients' mobility and guarantees personal privacy protection. Firstly, we
incorporate prior global geographic adjacency information to the local client
model, since the spatial correlation between locations is trained partially in
each client who has only a heterogeneous subset of the overall trajectories in
FL. We also introduce a novel aggregation method that minimizes the gap between
client models to solve the problem of client drift caused by differences
between client models when learning with their heterogeneous data. Lastly, we
probabilistically exclude clients with extremely heterogeneous data from the FL
process by focusing on clients who visit relatively diverse locations. We show
that FedGeo is superior to other FL methods for model performance in UNLP task.
We also validated our model in a real-world application using our own
customers' mobile phones and the FL agent system.