Bi-level Heterogeneous Learning for Time Series Foundation Models: A Federated Learning Approach

TL;DR

This paper introduces a federated learning approach for time series foundation models that effectively handles heterogeneity across domains and tasks, improving performance in diverse forecasting benchmarks.

Contribution

It proposes a bi-level heterogeneity-aware federated learning method that reduces domain conflicts and enhances cross-domain collaboration for training TSFMs from scratch.

Findings

Outperforms centralized and federated baselines in point and probabilistic forecasting.

Achieves competitive zero-shot performance at scale.

Effectively manages heterogeneity in diverse time series benchmarks.

Abstract

Heterogeneity in time series data is more pronounced than in vision or language, as temporal dynamics vary substantially across domains and tasks. Existing efforts on training time series foundation models (TSFMs) from scratch are often trained with mixed-batch strategies that merge large-scale datasets, which can cause gradient conflicts and degrade representation quality. To address this, we propose a fine-grained learning method that distills invariant knowledge from heterogeneous series while reducing cross-domain interference. We characterize heterogeneity at two levels: inter-domain and intra-domain. To tackle this bi-level heterogeneity, we design a federated learning method that mitigates intra-domain conflicts by enforcing domain-invariant and semantically consistent representations through local regularization, and addresses inter-domain discrepancies by enhancing cross-domain collaboration via domain-aware aggregation. Experiments across diverse benchmarks show that TSFMs trained with our method consistently outperform both centralized and federated TSFM baselines in point and probabilistic forecasting, while also achieving competitive zero-shot performance at scale, offering a flexible pathway for training TSFMs from scratch in heterogeneous environments.