Accurate traffic flow prediction plays an important role in intelligent transportation management and reducing traffic congestion for smart cities. Existing traffic flow prediction techniques using deep learning, mostly LSTMs, have achieved enormous success based on the large traffic flow datasets collected by governments and different organizations. Nevertheless, a lot of these datasets contain sensitive attributes that may relate to users’ private data. Hence, there is a need to develop an accurate traffic flow prediction mechanism that preserves users’ privacy. To address this challenge, we propose a federated learning-based temporal fusion transformer framework for traffic flow prediction which is a distributed machine learning approach where all the model updates are aggregated through an aggregation algorithm rather than sharing and storing the raw data in one centralized location. The proposed framework trains the data locally on client devices using temporal fusion transformers and differential privacy. Experiments show that the proposed framework can guarantee accuracy in predicting traffic flow for both the short and long term.