Copertina

Federated Weather Modeling on Sensor Data

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Chen, Shengchao, Long, Guodong
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Federated Prompt Learning for Weather Foundation Models on Devices
di: Chen, Shengchao, et al.
Pubblicazione: (2023)

Personalized Adapter for Large Meteorology Model on Devices: Towards Weather Foundation Models
di: Chen, Shengchao, et al.
Pubblicazione: (2024)

Federated Foundation Models on Heterogeneous Time Series
di: Chen, Shengchao, et al.
Pubblicazione: (2024)

FeDaL: Federated Dataset Learning for General Time Series Foundation Models
di: Chen, Shengchao, et al.
Pubblicazione: (2025)

Bi-level Heterogeneous Learning for Time Series Foundation Models: A Federated Learning Approach
di: Chen, Shengchao, et al.
Pubblicazione: (2026)

Discrete Prototypical Memories for Federated Time Series Foundation Models
di: Deng, Liwei, et al.
Pubblicazione: (2026)

Client-supervised Federated Learning: Towards One-model-for-all Personalization
di: Yan, Peng, et al.
Pubblicazione: (2024)

FedMerge: Federated Personalization via Model Merging
di: Chen, Shutong, et al.
Pubblicazione: (2025)

Federated Recommendation with Additive Personalization
di: Li, Zhiwei, et al.
Pubblicazione: (2023)

Multi-Level Additive Modeling for Structured Non-IID Federated Learning
di: Chen, Shutong, et al.
Pubblicazione: (2024)

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach
di: Yang, Yiyuan, et al.
Pubblicazione: (2025)

Bi-level Personalization for Federated Foundation Models: A Task-vector Aggregation Approach
di: Yang, Yiyuan, et al.
Pubblicazione: (2025)

Federated Low-Rank Adaptation for Foundation Models: A Survey
di: Yang, Yiyuan, et al.
Pubblicazione: (2025)

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices
di: Li, Hangyu, et al.
Pubblicazione: (2025)

Dual-Personalizing Adapter for Federated Foundation Models
di: Yang, Yiyuan, et al.
Pubblicazione: (2024)

Distilling A Universal Expert from Clustered Federated Learning
di: Leng, Zeqi, et al.
Pubblicazione: (2025)

A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendation
di: Li, Zhiwei, et al.
Pubblicazione: (2025)

Navigating the Future of Federated Recommendation Systems with Foundation Models
di: Li, Zhiwei, et al.
Pubblicazione: (2024)

Personalized Interpretation on Federated Learning: A Virtual Concepts approach
di: Yan, Peng, et al.
Pubblicazione: (2024)

Influence-oriented Personalized Federated Learning
di: Tan, Yue, et al.
Pubblicazione: (2024)

Learning Multi-Agent Communication from Graph Modeling Perspective
di: Hu, Shengchao, et al.
Pubblicazione: (2024)

Contrastive Learning Boosts Deterministic and Generative Models for Weather Data
di: Bailey, Nathan
Pubblicazione: (2026)

Federated Vision-Language-Recommendation with Personalized Fusion
di: Li, Zhiwei, et al.
Pubblicazione: (2024)

Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning
di: Ji, Shaoxiong, et al.
Pubblicazione: (2021)

Reconstruct the Pruned Model without Any Retraining
di: Wang, Pingjie, et al.
Pubblicazione: (2024)

Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach
di: Li, Zhiwei, et al.
Pubblicazione: (2024)

Free Lunch for Federated Remote Sensing Target Fine-Grained Classification: A Parameter-Efficient Framework
di: Chen, Shengchao, et al.
Pubblicazione: (2024)

Agent-Agnostic Centralized Training for Decentralized Multi-Agent Cooperative Driving
di: Yan, Shengchao, et al.
Pubblicazione: (2024)

On Distinguishing Capability Elicitation from Capability Creation in Post-Training: A Free-Energy Perspective
di: Li, Yuhao, et al.
Pubblicazione: (2026)

Communication Learning in Multi-Agent Systems from Graph Modeling Perspective
di: Hu, Shengchao, et al.
Pubblicazione: (2024)

RefProtoFL: Communication-Efficient Federated Learning via External-Referenced Prototype Alignment
di: Wu, Hongyue, et al.
Pubblicazione: (2026)

Towards Scaling Law Analysis For Spatiotemporal Weather Data
di: Kiefer, Alexander, et al.
Pubblicazione: (2026)

Using Neural Networks for Data Cleaning in Weather Datasets
di: Hanslope, Jack R. P., et al.
Pubblicazione: (2024)

WeatherGNN: Exploiting Meteo- and Spatial-Dependencies for Local Numerical Weather Prediction Bias-Correction
di: Wu, Binqing, et al.
Pubblicazione: (2023)

Scaling Laws of Global Weather Models
di: Yu, Yuejiang, et al.
Pubblicazione: (2026)

BYE: Build Your Encoder with One Sequence of Exploration Data for Long-Term Dynamic Scene Understanding
di: Huang, Chenguang, et al.
Pubblicazione: (2024)

Forecasting Fails: Unveiling Evasion Attacks in Weather Prediction Models
di: Arif, Huzaifa, et al.
Pubblicazione: (2025)

Data-Efficient Ensemble Weather Forecasting with Diffusion Models
di: Valencia, Kevin, et al.
Pubblicazione: (2025)

Exploring the Use of Machine Learning Weather Models in Data Assimilation
di: Tian, Xiaoxu, et al.
Pubblicazione: (2024)

FedRAIN-Lite: Federated Reinforcement Algorithms for Improving Idealised Numerical Weather and Climate Models
di: Nath, Pritthijit, et al.
Pubblicazione: (2025)