mlsanityData to 14 Oct 2026

Federated Prompt Learning for Weather Foundation Models on Devices

arXiv 2305.14244 ↗First posted 23 May 2023cs.LGcited by 17

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Abstract (arXiv)

On-device intelligence for weather forecasting uses local deep learning models to analyze weather patterns without centralized cloud computing, holds significance for supporting human activates. Federated Learning is a promising solution for such forecasting by enabling collaborative model training without sharing raw data. However, it faces three main challenges that hinder its reliability: (1) data heterogeneity among devices due to geographic differences; (2) data homogeneity within individual devices and (3) communication overload from sending large model parameters for collaboration. To address these challenges, this paper propose Federated Prompt Learning for Weather Foundation Models on Devices (FedPoD), which enables devices to obtain highly customized models while maintaining communication efficiency. Concretely, our Adaptive Prompt Tuning leverages lightweight prompts guide frozen foundation model to generate more precise predictions, also conducts prompt-based multi-level communication to encourage multi-source knowledge fusion and regulate optimization. Additionally, Dynamic Graph Modeling constructs graphs from prompts, prioritizing collaborative training among devices with similar data distributions to against heterogeneity. Extensive experiments demonstrates FedPoD leads the performance among state-of-the-art baselines across various setting in real-world on-device weather forecasting datasets.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). Builds on 33 of its 50 references that are in the map. First posted on arXiv 23 May 2023.

Builds on

33 of 50 references.

References · 33

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  5. 2020End-to-End Object Detection with Transformerscited by 7,414
  6. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  7. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
  8. 2022Visual Prompt Tuningcited by 1,681
  9. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,676
  10. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,436
  11. 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,358
  12. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,210
21 more
  1. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
  2. 2020Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inferencecited by 938
  3. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 672
  4. 2023A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPTcited by 619
  5. 2020Personalized Federated Learning with Moreau Envelopescited by 618
  6. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 571
  7. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 541
  8. 2020Adaptive Personalized Federated Learningcited by 358
  9. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 283
  10. 2022Towards Robust Blind Face Restoration with Codebook Lookup Transformercited by 231
  11. 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
  12. 2022Unified Vision and Language Prompt Learningcited by 148
  13. 2022Federated Learning from Pre-Trained Models: A Contrastive Learning Approachcited by 142
  14. 2022PromptFL: Let Federated Participants Cooperatively Learn Prompts Instead of Models -- Federated Learning in Age of Foundation Modelcited by 134
  15. 2020Lower Bounds and Optimal Algorithms for Personalized Federated Learningcited by 123
  16. 2021Federated Evaluation and Tuning for On-Device Personalization: System Design & Applicationscited by 85
  17. 2022FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learningcited by 66
  18. 2023Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Datacited by 40
  19. 2022Personalized Federated Learning With Graphcited by 29
  20. 2023Visual Prompt Based Personalized Federated Learningcited by 16
  21. 2023TempEE: Temporal-Spatial Parallel Transformer for Radar Echo Extrapolation Beyond Auto-Regressioncited by 12

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 6; 2025: 2; 2026: 7.

17 papers.

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  1. 2023Foundation Models for Weather and Climate Data Understanding: A Comprehensive Surveycited by 34
  2. 2023Text-driven Prompt Generation for Vision-Language Models in Federated Learningcited by 17
  3. 2024Federated Foundation Models on Heterogeneous Time Seriescited by 13
  4. 2024WeatherGFM: Learning A Weather Generalist Foundation Model via In-context Learningcited by 12
  5. 2025FeDaL: Federated Dataset Learning for General Time Series Foundation Modelscited by 10
  6. 2024Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approachcited by 9
  7. 2024Free Lunch for Federated Remote Sensing Target Fine-Grained Classification: A Parameter-Efficient Frameworkcited by 6
  8. 2024How Much Can Time-related Features Enhance Time Series Forecasting?cited by 6
  9. 2026Bi-level Heterogeneous Learning for Time Series Foundation Models: A Federated Learning Approachcited by 2
  10. 2026OmniFM: Toward Modality-Robust and Task-Agnostic Federated Learning for Heterogeneous Medical Imagingcited by 1
  11. 2026Spectral-Aware Text-to-Time Series Generation with Billion-Scale Multimodal Meteorological Datacited by 1
  12. 2026Discrete Prototypical Memories for Federated Time Series Foundation Modelscited by 1
5 more
  1. 2024Less is more: Ensemble Learning for Retinal Disease Recognition Under Limited Resourcescited by 0
  2. 2025Federated Cross-Modal Style-Aware Prompt Generationcited by 0
  3. 2026Optimizing Multi-Agent Weather Captioning via Text Gradient Descent: A Training-Free Approach with Consensus-Aware Gradient Fusioncited by 0
  4. 2026Federated Weather Modeling on Sensor Datacited by 0
  5. 2026Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imagingcited by 0