Multi-Level Additive Modeling for Structured Non-IID Federated Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Chen, Shutong, Zhou, Tianyi, Long, Guodong, Ma, Jie, Jiang, Jing, Zhang, Chengqi |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
FedMerge: Federated Personalization via Model Merging
by: Chen, Shutong, et al.
Published: (2025)
by: Chen, Shutong, et al.
Published: (2025)
Federated Foundation Models on Heterogeneous Time Series
by: Chen, Shengchao, et al.
Published: (2024)
by: Chen, Shengchao, et al.
Published: (2024)
Federated Prompt Learning for Weather Foundation Models on Devices
by: Chen, Shengchao, et al.
Published: (2023)
by: Chen, Shengchao, et al.
Published: (2023)
Federated Recommendation with Additive Personalization
by: Li, Zhiwei, et al.
Published: (2023)
by: Li, Zhiwei, et al.
Published: (2023)
Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach
by: Li, Zhiwei, et al.
Published: (2024)
by: Li, Zhiwei, et al.
Published: (2024)
Influence-oriented Personalized Federated Learning
by: Tan, Yue, et al.
Published: (2024)
by: Tan, Yue, et al.
Published: (2024)
Personalized Adapter for Large Meteorology Model on Devices: Towards Weather Foundation Models
by: Chen, Shengchao, et al.
Published: (2024)
by: Chen, Shengchao, et al.
Published: (2024)
Federated Low-Rank Adaptation for Foundation Models: A Survey
by: Yang, Yiyuan, et al.
Published: (2025)
by: Yang, Yiyuan, et al.
Published: (2025)
A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendation
by: Li, Zhiwei, et al.
Published: (2025)
by: Li, Zhiwei, et al.
Published: (2025)
Navigating the Future of Federated Recommendation Systems with Foundation Models
by: Li, Zhiwei, et al.
Published: (2024)
by: Li, Zhiwei, et al.
Published: (2024)
Federated Vision-Language-Recommendation with Personalized Fusion
by: Li, Zhiwei, et al.
Published: (2024)
by: Li, Zhiwei, et al.
Published: (2024)
What Hides behind Unfairness? Exploring Dynamics Fairness in Reinforcement Learning
by: Deng, Zhihong, et al.
Published: (2024)
by: Deng, Zhihong, et al.
Published: (2024)
Federated Adapter on Foundation Models: An Out-Of-Distribution Approach
by: Yang, Yiyuan, et al.
Published: (2025)
by: Yang, Yiyuan, et al.
Published: (2025)
Bi-level Heterogeneous Learning for Time Series Foundation Models: A Federated Learning Approach
by: Chen, Shengchao, et al.
Published: (2026)
by: Chen, Shengchao, et al.
Published: (2026)
FeDaL: Federated Dataset Learning for General Time Series Foundation Models
by: Chen, Shengchao, et al.
Published: (2025)
by: Chen, Shengchao, et al.
Published: (2025)
FedSKC: Federated Learning with Non-IID Data via Structural Knowledge Collaboration
by: Wang, Huan, et al.
Published: (2025)
by: Wang, Huan, et al.
Published: (2025)
MultiConfederated Learning: Inclusive Non-IID Data handling with Decentralized Federated Learning
by: Duchesne, Michael, et al.
Published: (2024)
by: Duchesne, Michael, et al.
Published: (2024)
Understanding Federated Learning from IID to Non-IID dataset: An Experimental Study
by: Seo, Jungwon, et al.
Published: (2025)
by: Seo, Jungwon, et al.
Published: (2025)
Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data
by: Liao, Xinting, et al.
Published: (2024)
by: Liao, Xinting, et al.
Published: (2024)
Tackling the Non-IID Issue in Heterogeneous Federated Learning by Gradient Harmonization
by: Zhang, Xinyu, et al.
Published: (2023)
by: Zhang, Xinyu, et al.
Published: (2023)
Sequential Federated Learning in Hierarchical Architecture on Non-IID Datasets
by: Yan, Xingrun, et al.
Published: (2024)
by: Yan, Xingrun, et al.
Published: (2024)
Federated Weather Modeling on Sensor Data
by: Chen, Shengchao, et al.
Published: (2026)
by: Chen, Shengchao, et al.
Published: (2026)
Heterogeneity-Guided Client Sampling: Towards Fast and Efficient Non-IID Federated Learning
by: Chen, Huancheng, et al.
Published: (2023)
by: Chen, Huancheng, et al.
Published: (2023)
Studying Various Activation Functions and Non-IID Data for Machine Learning Model Robustness
by: Dang, Long, et al.
Published: (2025)
by: Dang, Long, et al.
Published: (2025)
Federated Active Learning Under Extreme Non-IID and Global Class Imbalance
by: Zong, Chen-Chen, et al.
Published: (2026)
by: Zong, Chen-Chen, et al.
Published: (2026)
Learning Critically: Selective Self Distillation in Federated Learning on Non-IID Data
by: He, Yuting, et al.
Published: (2025)
by: He, Yuting, et al.
Published: (2025)
Dataset Distillation-based Hybrid Federated Learning on Non-IID Data
by: Shi, Xiufang, et al.
Published: (2024)
by: Shi, Xiufang, et al.
Published: (2024)
Momentum Benefits Non-IID Federated Learning Simply and Provably
by: Cheng, Ziheng, et al.
Published: (2023)
by: Cheng, Ziheng, et al.
Published: (2023)
Decoupled Federated Learning on Long-Tailed and Non-IID data with Feature Statistics
by: Chen, Zhuoxin, et al.
Published: (2024)
by: Chen, Zhuoxin, et al.
Published: (2024)
Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Sampling
by: Wong, Hui Yeok, et al.
Published: (2025)
by: Wong, Hui Yeok, et al.
Published: (2025)
FedPrism: Adaptive Personalized Federated Learning under Non-IID Data
by: Kumbhakar, Prakash, et al.
Published: (2026)
by: Kumbhakar, Prakash, et al.
Published: (2026)
Multi-Modal Federated Learning for Cancer Staging over Non-IID Datasets with Unbalanced Modalities
by: Borazjani, Kasra, et al.
Published: (2024)
by: Borazjani, Kasra, et al.
Published: (2024)
FedCure: Mitigating Participation Bias in Semi-Asynchronous Federated Learning with Non-IID Data
by: Chen, Yue, et al.
Published: (2025)
by: Chen, Yue, et al.
Published: (2025)
A Thorough Assessment of the Non-IID Data Impact in Federated Learning
by: Jimenez-Gutierrez, Daniel M., et al.
Published: (2025)
by: Jimenez-Gutierrez, Daniel M., et al.
Published: (2025)
Personalized Interpretation on Federated Learning: A Virtual Concepts approach
by: Yan, Peng, et al.
Published: (2024)
by: Yan, Peng, et al.
Published: (2024)
Sample Selection Using Multi-Task Autoencoders in Federated Learning with Non-IID Data
by: Ardıç, Emre, et al.
Published: (2026)
by: Ardıç, Emre, et al.
Published: (2026)
Federated Multi-Task Learning on Non-IID Data Silos: An Experimental Study
by: Yang, Yuwen, et al.
Published: (2024)
by: Yang, Yuwen, et al.
Published: (2024)
Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Data
by: Sun, Yuchang, et al.
Published: (2021)
by: Sun, Yuchang, et al.
Published: (2021)
A Client-level Assessment of Collaborative Backdoor Poisoning in Non-IID Federated Learning
by: Lai, Phung, et al.
Published: (2025)
by: Lai, Phung, et al.
Published: (2025)
Bi-level Personalization for Federated Foundation Models: A Task-vector Aggregation Approach
by: Yang, Yiyuan, et al.
Published: (2025)
by: Yang, Yiyuan, et al.
Published: (2025)
Similar Items
-
FedMerge: Federated Personalization via Model Merging
by: Chen, Shutong, et al.
Published: (2025) -
Federated Foundation Models on Heterogeneous Time Series
by: Chen, Shengchao, et al.
Published: (2024) -
Federated Prompt Learning for Weather Foundation Models on Devices
by: Chen, Shengchao, et al.
Published: (2023) -
Federated Recommendation with Additive Personalization
by: Li, Zhiwei, et al.
Published: (2023) -
Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach
by: Li, Zhiwei, et al.
Published: (2024)