Academic

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

arXiv:2604.06727v1 Announce Type: new 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. Experim

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Shengchao Chen, Guodong Long, Dikai Liu, Jing Jiang · · 1 min read · 25 views
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arXiv:2604.06727v1 Announce Type: new 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.

Executive Summary

This article introduces a novel federated learning (FL) approach to train Time Series Foundation Models (TSFMs) from scratch, specifically addressing the pronounced bi-level heterogeneity inherent in time series data. The proposed method tackles inter-domain and intra-domain discrepancies by employing local regularization to enforce domain-invariant and semantically consistent representations, and domain-aware aggregation to enhance cross-domain collaboration. This innovative strategy aims to distill invariant knowledge while minimizing gradient conflicts and cross-domain interference, which are common pitfalls of traditional mixed-batch training. Experimental results demonstrate superior performance in forecasting tasks and competitive zero-shot capabilities, positioning it as a promising pathway for robust TSFM development in diverse and decentralized environments.

Key Points

  • ▸ Time series data exhibits unique bi-level heterogeneity (inter-domain and intra-domain) that challenges traditional TSFM training.
  • ▸ Existing mixed-batch TSFM training strategies often suffer from gradient conflicts and degraded representation quality due to heterogeneity.
  • ▸ The proposed federated learning method addresses intra-domain conflicts via local regularization for domain-invariant and semantically consistent representations.
  • ▸ Inter-domain discrepancies are mitigated through domain-aware aggregation, fostering enhanced cross-domain collaboration.
  • ▸ The method achieves superior performance in point and probabilistic forecasting and competitive zero-shot capabilities compared to baselines.

Merits

Novelty in Addressing Heterogeneity

The explicit recognition and bi-level decomposition of time series heterogeneity (inter- and intra-domain) is a significant conceptual advancement, providing a more granular understanding of the challenges.

Innovative Federated Learning Design

The tailored FL architecture, combining local regularization with domain-aware aggregation, offers a sophisticated and practical solution to the identified bi-level heterogeneity, moving beyond generic FL applications.

Robust Performance Validation

The reported consistent outperformance against both centralized and federated TSFM baselines across diverse benchmarks, including zero-shot capabilities, strongly validates the method's efficacy.

Practical Applicability

The method offers a flexible pathway for training TSFMs from scratch in heterogeneous and potentially decentralized environments, which aligns with real-world data governance and privacy considerations.

Demerits

Computational Overhead

Federated learning, especially with intricate local regularization and domain-aware aggregation, can introduce significant computational and communication overheads, which may limit scalability in extremely large-scale deployments.

Hyperparameter Sensitivity

The success of local regularization and domain-aware aggregation likely depends on carefully tuned hyperparameters, and the robustness to different parameter settings is not fully explored in the abstract.

Privacy Guarantees (Implicit)

While FL inherently offers privacy benefits, the abstract doesn't explicitly detail the specific privacy-preserving mechanisms or formal guarantees, which are crucial in many real-world applications of FL.

Generalizability Across All Time Series Types

While tested on 'diverse benchmarks,' the article does not specify the full spectrum of time series data types (e.g., financial, medical, IoT sensor data) and their varying temporal characteristics, which might impact generalizability.

Expert Commentary

This paper represents a significant stride in the burgeoning field of Time Series Foundation Models, adeptly tackling the intrinsic challenges posed by data heterogeneity. The bi-level characterization of heterogeneity is a particularly insightful contribution, moving beyond a monolithic view to provide a more nuanced understanding of the problem space. The proposed federated learning paradigm, with its dual focus on local regularization for intra-domain coherence and domain-aware aggregation for inter-domain collaboration, demonstrates a sophisticated architectural design. This approach not only promises enhanced model performance but also addresses critical real-world constraints such as data privacy and distributed computation. The reported zero-shot capabilities are especially compelling, suggesting a pathway towards truly generalizable TSFMs. Future research should delve into the computational efficiency of this approach across diverse network topologies and explore formal privacy guarantees, which are paramount for widespread adoption in regulated industries. The robustness of the method to varying degrees of heterogeneity and data sparsity also warrants further investigation.

Recommendations

  • ✓ Conduct a thorough analysis of the computational and communication overheads, especially comparing against centralized training and simpler FL baselines, to provide a clearer picture of scalability.
  • ✓ Elaborate on the specific privacy-preserving mechanisms implemented (e.g., differential privacy, secure multi-party computation) and provide formal privacy guarantees where applicable.
  • ✓ Investigate the sensitivity of the model's performance to hyperparameter choices for local regularization and domain-aware aggregation, offering guidance on optimal tuning strategies.
  • ✓ Expand the experimental validation to include a wider array of time series data types and domains (e.g., high-frequency financial data, irregularly sampled medical records) to assess generalizability more comprehensively.
  • ✓ Explore the interpretability of the learned domain-invariant representations and how they contribute to forecasting accuracy and zero-shot capabilities.

Sources

Original: arXiv - cs.LG

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