  Traffic flow forecasting (TFF) is of great importance to the construction of
Intelligent Transportation Systems (ITS). To mitigate communication burden and
tackle with the problem of privacy leakage aroused by centralized forecasting
methods, Federated Learning (FL) has been applied to TFF. However, existing
FL-based approaches employ batch learning manner, which makes the pre-trained
models inapplicable to subsequent traffic data, thus exhibiting subpar
prediction performance. In this paper, we perform the first study of
forecasting traffic flow adopting Online Learning (OL) manner in FL framework
and then propose a novel prediction method named Online Spatio-Temporal
Correlation-based Federated Learning (FedOSTC), aiming to guarantee performance
gains regardless of traffic fluctuation. Specifically, clients employ Gated
Recurrent Unit (GRU)-based encoders to obtain the internal temporal patterns
inside traffic data sequences. Then, the central server evaluates spatial
correlation among clients via Graph Attention Network (GAT), catering to the
dynamic changes of spatial closeness caused by traffic fluctuation.
Furthermore, to improve the generalization of the global model for upcoming
traffic data, a period-aware aggregation mechanism is proposed to aggregate the
local models which are optimized using Online Gradient Descent (OGD) algorithm
at clients. We perform comprehensive experiments on two real-world datasets to
validate the efficiency and effectiveness of our proposed method and the
numerical results demonstrate the superiority of FedOSTC.
