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. Federated learning (FL) offers a privacy-preserving alternative by training shared models without exchanging raw data, but its effectiveness for survival modeling under realistic, heterogeneous conditions remains insufficiently understood. This paper presents a systematic, multi-model evaluation of federat
The increasing maturity of federated learning techniques combined with persistent data privacy regulations is converging to enable new applications in sensitive domains like healthcare.
This development allows for collaborative AI model training across institutions without compromising patient privacy, potentially accelerating medical research and improving diagnostic capabilities.
Healthcare institutions can now pool data insights to train more robust AI models for critical applications like survival analysis, without the need for centralized, privacy-compromising data lakes.
- · Healthcare institutions
- · Federated learning platforms
- · AI-driven diagnostic companies
- · Patients
- · Traditional centralized data analytics providers
- · Organizations relying solely on small, localized datasets
Improved accuracy and generalizability of AI models in healthcare due to larger, more diverse training cohorts.
Increased adoption of federated learning paradigms across other privacy-sensitive industries, such as finance or government.
The development of new regulatory frameworks specifically designed to enable and govern federated data collaboration globally.
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