  Mobile devices and the Internet of Things (IoT) devices nowadays generate a
large amount of heterogeneous spatial-temporal data. It remains a challenging
problem to model the spatial-temporal dynamics under privacy concern. Federated
learning (FL) has been proposed as a framework to enable model training across
distributed devices without sharing original data which reduce privacy concern.
Personalized federated learning (PFL) methods further address data heterogenous
problem. However, these methods don't consider natural spatial relations among
nodes. For the sake of modeling spatial relations, Graph Neural Netowork (GNN)
based FL approach have been proposed. But dynamic spatial-temporal relations
among edge nodes are not taken into account. Several approaches model
spatial-temporal dynamics in a centralized environment, while less effort has
been made under federated setting. To overcome these challeges, we propose a
novel Federated Adaptive Spatial-Temporal Attention (FedASTA) framework to
model the dynamic spatial-temporal relations. On the client node, FedASTA
extracts temporal relations and trend patterns from the decomposed terms of
original time series. Then, on the server node, FedASTA utilize trend patterns
from clients to construct adaptive temporal-spatial aware graph which captures
dynamic correlation between clients. Besides, we design a masked spatial
attention module with both static graph and constructed adaptive graph to model
spatial dependencies among clients. Extensive experiments on five real-world
public traffic flow datasets demonstrate that our method achieves state-of-art
performance in federated scenario. In addition, the experiments made in
centralized setting show the effectiveness of our novel adaptive graph
construction approach compared with other popular dynamic spatial-temporal
aware methods.
