arXiv Machine Learning
Making Cross-Continental Federated Learning Repeatable with FLIP: a Multi-Application Study
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arXiv Machine Learning
Sep 18
Multi-center Medical Data Mining with FL-Net - A One-stop Shop for Federated Learning
FL-Net is a federated learning framework designed for medical research that addresses five key requirements identified in the literature. It offers modular data harmonization, discovery, disclosure control, versioned tools, and containerized workflow execution, enabling reuse of harmonized data and workflows across studies. The framework was evaluated on MIMIC and US-130 datasets, supporting up to 50 concurrent clients and demonstrating reproducible, audited federated workflows.
arXiv Machine Learning
Jul 14
Toward Production-Ready Federated Learning in Healthcare: Privacy, Orchestration, and Governance in MLOps
arXiv:2607. 10467v1 Announce Type: cross Abstract: Healthcare organizations often cannot freely centralize patient data because medical records are sensitive, regulated, and institutionally controlled.
arXiv Machine Learning
Aug 5
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.
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 Machine Learning
Jun 24
Federated Survival Analysis in Healthcare: A Multi-Model Evaluation on Cross-Institutional Heterogeneous Breast Cancer Data
arXiv:2606. 23871v1 Announce Type: new Abstract: Survival analysis is central to clinical decision-making, yet reliable time-to-event models require large, diverse cohorts that are rarely available at a single institution, while privacy regulations restrict the centralization of patient data.
arXiv Machine Learning
Sep 22
A Federated Artificial Intelligence Framework for Optimizing Pancreatic Cancer Treatment - Strategy Update
arXiv:2609.24718v1 Announce Type: new Abstract: While a centralized approach involving patient consent to collect and analyze data centrally would theoretically offer the best data quality and predic...