2/18/2026 | USA | technology | ✓ Verified - arxiv.org

Hybrid Federated and Split Learning for Privacy Preserving Clinical Prediction and Treatment Optimization

#Hybrid FL-SL #Privacy Preserving #Split Learning #Federated Learning #Clinical Prediction #Decision Support #Patient Data Governance #Healthcare AI #Model Collaboration

📌 Key Takeaways

Integrates Federated Learning and Split Learning to preserve patient privacy.
Keeps feature‑extraction processing on local clients and hosts prediction heads in a central coordinator.
Enables model training and inference across multiple hospitals without raw data exchange.
Supports real‑time, decision‑oriented clinical prediction and treatment optimization.
Demonstrates a practical approach to compliant, collaborative healthcare AI.
Can be adapted to various governance frameworks such as GDPR or HIPAA.

📖 Full Retelling

Hybrid Federated and Split Learning for Privacy Preserving Clinical Prediction and Treatment Optimization is a framework created for clinicians and researchers across multiple healthcare institutions. The study, published on arXiv in February 2026, introduces a combination of Federated Learning (FL) and Split Learning (SL) to build decision-oriented clinical models while keeping patient data on local servers. The research seeks to overcome governance and privacy regulations that prohibit the sharing of raw patient records, allowing institutions to collaborate on predictive analytics without exposing sensitive information.

🏷️ Themes

Data Privacy, Collaborative Machine Learning, Medical AI, Federated Learning, Split Learning, Clinical Decision Support, Healthcare Governance

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Deep Analysis

Why It Matters

This hybrid approach allows hospitals to collaborate on predictive models without sharing sensitive patient data, addressing legal and ethical barriers. It can improve treatment decisions by leveraging diverse datasets while maintaining privacy.

Context & Background

Federated Learning keeps raw data local while sharing model updates
Split Learning partitions the model so only intermediate activations are exchanged
Combining both reduces communication overhead and protects feature privacy

What Happens Next

Researchers will test the framework on larger multi-institution trials and refine the architecture for real-world deployment. Regulatory bodies may adopt guidelines for such privacy-preserving collaborations.

Frequently Asked Questions

How does the hybrid model keep patient data private?

Clients keep the feature-extraction part of the model locally and only send encrypted intermediate results to the coordinator, which hosts the prediction head.

Will this approach replace traditional data sharing?

It offers an alternative that meets privacy regulations, but it does not eliminate the need for data governance and oversight.

Original Source

              arXiv:2602.15304v1 Announce Type: cross 
Abstract: Collaborative clinical decision support is often constrained by governance and privacy rules that prevent pooling patient-level records across institutions. We present a hybrid privacy-preserving framework that combines Federated Learning (FL) and Split Learning (SL) to support decision-oriented healthcare modeling without raw-data sharing. The approach keeps feature-extraction trunks on clients while hosting prediction heads on a coordinating s
            

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Source

arxiv.org