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Deep learning (DL) models have established remarkable success in traffic state estimation (TSE); however, the widely-studied approaches' centralized nature presents deployment challenges in Intelligent Transportation Systems (ITS), especially regarding data ownership, privacy, and response times. Online models trained using Federated Learning (FL) approaches can harvest the rich real-time data streams in ITS for continuously updating the TSE models. Though, there are limitations during the online learning process, such as model drift. In this paper, we propose a variable learning rate (VLR) scheduling approach that integrates online FL with cooperative edge computing (CEC). Continuously collected local traffic data by roadside units (RSUs) and connected sensors train localized models, and only the updated model parameters are shared instead of raw data. VLR adjustments within the Adaptive Gradient …