A Continuous-Time On-Device Federated Learning Network
Yimin Dai, Rui Tan · IEEE Transactions on Networking · 2026
Time series is an important form of data generated by Internet-of-Things (IoT) devices. Closed-form continuous-time (CFC) neural networks offer superior expressivity for modeling time series data compared with recurrent neural networks. Additionally, their lower training and inference overhead make them well-suited for deployment on microcontroller-based IoT devices. This paper introduces FedCFC, an on-device federated learning network that operates based on the CFC models distributed across IoT devices. FedCFC incorporates a novel and communication-efficient aggregation strategy designed to mitigate the effects of class distribution imbalances across the participating IoT devices’ training data. The strategy is designed based on a new property of CFC identified in this paper, i.e., the insensitivity of a sub-model of CFC with respect to training data’s class distribution shift. Extensive evaluation with multiple time series datasets demonstrates that FedCFC attains comparable or superior accuracy while achieving a 7.6× to 11× reduction in communication overhead compared with recent federated learning approaches designed to address the class distribution skew problem. Furthermore, deployments of FedCFC on four IoT platforms highlight its suitability for resource-constrained devices with as little as 256 kB of memory or even less.