Rethinking Federated Time Series Pretraining Through Shared Latent Dynamics

Pretraining a time series model typically relies on centralizing large and diverse collections of data, an assumption incompatible with privacy-sensitive domains such as power grids, healthcare, finance, and distributed sensor networks. Beyond privacy, Federated learning (FL) addresses client heterogeneity by combining personalized local models with a shared global model, placing the choice of shared representation at the center of the model design. Existing FL approaches for time series make it encode observation-level temporal patterns, which vary substantially across heterogeneous clients. We instead identify latent state transitions as a more transferable object for federation and propose FedState, a federated pretraining framework that keeps client models private and shares only a latent transition operator. The operator is indexed by elapsed physical time rather than patch position and is learned entirely in latent state space, allowing clients with different sampling intervals, channel counts, value scales, and local architectures to learn shared dynamics without aligning their observations. Only the transition operator is exchanged, a small fraction of the model parameters and of the per-round communication of full-model federation. Extensive experiments across eight benchmarks demonstrate that FedState outperforms full-model federation, advanced federated time series pretraining methods, and centralized models in both deterministic and probabilistic forecasting, including zero-shot transfer to unseen domains without target-domain adaptation.