arXiv Machine Learning
Learning From Static Context in Federated Time Series Forecasting
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Aug 4
FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting
arXiv:2608. 01290v1 Announce Type: new Abstract: Time-series foundation models (TSFMs) such as Chronos have demonstrated strong forecasting capabilities across domains, yet adapting them to institutionally fragmented settings, where data cannot be centralized due to regulatory, competitive, or sovereignty constraints, remains unexplored.
arXiv AI
Aug 6
Personalized Federated Sparse Adaptation of Time-Series Foundation Models
arXiv:2608. 04695v1 Announce Type: cross Abstract: Federated adaptation of time-series foundation models (TSFMs) is attractive for building energy forecasting because meter data are private, distributed, and highly non-IID.
arXiv Machine Learning
Jul 7
FedFFT: Taming Client Drift in Federated SAM via Spectral Perturbation Filtering
arXiv:2607. 04170v1 Announce Type: new Abstract: Federated Learning (FL) enables decentralized training without data sharing, but suffers from statistical heterogeneity across clients, leading to client drift, poor generalization, and sharp minima compared to centralized training.
Hugging Face Trending Papers
Aug 5
Personalized Federated Sparse Adaptation of Time-Series Foundation Models
Federated adaptation of time-series foundation models (TSFMs) is attractive for building energy forecasting because meter data are private, distributed, and highly non-IID. However, a single parameter-sharing strategy is unlikely to serve all pretrained TSFMs or building clients: fully shared adapters can suppress building-specific temporal behavior, while fully local adaptation discards cross-building transfer.
arXiv AI
Aug 18
FedImp: Enhancing Federated Learning Convergence with Impurity-Based Weighting
arXiv:2608. 14654v1 Announce Type: cross Abstract: Federated Learning (FL) is a collaborative paradigm that enables multiple devices to train a global model while preserving local data privacy.
arXiv Machine Learning
Oct 16
Sparse Federated Echo State Networks for Time-Series Classification via Smooth-L1 Regularization
The paper introduces Smooth‑L1 regularized Echo State Networks (SL1ESN) that encourage sparsity in the readout layer while remaining differentiable for gradient‑based training. Using a twice‑differentiable Smooth‑L1 approximation, SL1ESN is trained centrally and via a second‑order federated aggregation scheme, achieving 75–95% sparsity across seven time‑series classification benchmarks. The resulting sparse models reduce storage, inference cost, and communication overhead, with only minor accuracy loss compared to dense baselines.