arXiv AI
FediLoRA: Practical Federated Fine-Tuning of Foundation Models Under Missing-Modality Constraints
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FedCARE: A Multi-Objective Personalised Federated Learning Framework for Smart Healthcare
arXiv:2608. 03498v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training across distributed healthcare institutions without centralising sensitive patient data.
arXiv AI
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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 AI
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FedPS: Federated Preprocessing for structured data via aggregated Statistics
FedPS is a federated preprocessing framework that uses aggregated statistics to address missing values, inconsistent formats, and heterogeneous feature scales in structured data. It employs data-sketching techniques to summarize local datasets efficiently, enabling federated algorithms for feature scaling, encoding, discretization, and missing-value imputation. The framework also extends preprocessing-related models, such as Bayesian Linear Regression, to both horizontal and vertical federated learning settings, offering communication‑efficient and consistent pipelines for practical deployments.
Hugging Face Trending Papers
Aug 4
FedCARE: A Multi-Objective Personalised Federated Learning Framework for Smart Healthcare
Federated Learning (FL) enables collaborative model training across distributed healthcare institutions without centralising sensitive patient data. However, real-world healthcare federations are often characterised not only by non-IID data, but also by heterogeneous clinical objectives and partially overlapping feature spaces.
arXiv AI
Oct 7
FedCoT: Communication-Efficient Federated Reasoning Enhancement for Large Language Models
arXiv:2508.10020v2 Announce Type: replace-cross Abstract: Enhancing LLM reasoning in federated settings is nontrivial due to stringent computational, communication, and privacy constraints, especiall...
arXiv Machine Learning
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FedADB: Class Anchor-Driven Dual-Branch Federated Learning for Mitigating Forgetting
arXiv:2608. 15310v1 Announce Type: cross Abstract: Multimodal data collected by heterogeneous devices are used for collaborative training, where federated learning (FL) serves as a key paradigm for effective distributed modeling with data privacy preservation.