Hybrid Federated and Split Learning for Privacy Preserving Clinical Prediction and Treatment Optimization
arXiv:2602.15304v1 Announce Type: new 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 server, enabling shared representation learning and exposing an explicit collaboration boundary where privacy controls can be applied. Rather than assuming distributed training is inherently private, we audit leakage empirically using membership inference on cut-layer representations and study lightweight defenses based on activation clipping and additive Gaussian noise. We evaluate across three public clinical datasets under non-IID client partitions using a unified pipeline and assess performance
arXiv:2602.15304v1 Announce Type: new 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 server, enabling shared representation learning and exposing an explicit collaboration boundary where privacy controls can be applied. Rather than assuming distributed training is inherently private, we audit leakage empirically using membership inference on cut-layer representations and study lightweight defenses based on activation clipping and additive Gaussian noise. We evaluate across three public clinical datasets under non-IID client partitions using a unified pipeline and assess performance jointly along four deployment-relevant axes: factual predictive utility, uplift-based ranking under capacity constraints, audited privacy leakage, and communication overhead. Results show that hybrid FL-SL variants achieve competitive predictive performance and decision-facing prioritization behavior relative to standalone FL or SL, while providing a tunable privacy-utility trade-off that can reduce audited leakage without requiring raw-data sharing. Overall, the work positions hybrid FL-SL as a practical design space for privacy-preserving healthcare decision support where utility, leakage risk, and deployment cost must be balanced explicitly.
Executive Summary
This article presents a hybrid federated and split learning framework, dubbed FL-SL, for privacy-preserving clinical prediction and treatment optimization. By combining the strengths of federated learning and split learning, the authors aim to enable collaborative clinical decision support while ensuring patient-level data remains decentralized and private. The proposed framework enables shared representation learning and allows for explicit control over data collaboration boundaries, facilitating a tunable privacy-utility trade-off. Empirical evaluation across three public clinical datasets demonstrates competitive predictive performance and decision-facing prioritization behavior relative to standalone FL or SL, while reducing audited leakage without requiring raw-data sharing.
Key Points
- ▸ Hybrid Federated and Split Learning (FL-SL) framework for privacy-preserving clinical prediction and treatment optimization
- ▸ Shared representation learning and explicit control over data collaboration boundaries
- ▸ Tunable privacy-utility trade-off and reduced audited leakage without raw-data sharing
Merits
Strength in Addressing Privacy Concerns
The FL-SL framework effectively addresses the critical issue of data privacy in collaborative clinical decision support, leveraging Federated Learning and Split Learning to minimize the risk of data breaches and unauthorized access.
Flexibility in Utility-Preservation Trade-Off
The proposed framework offers a flexible balance between predictive utility and privacy leakage, allowing practitioners to adjust the trade-off according to their specific needs and constraints.
Competitive Performance with FL and SL
Empirical evaluation demonstrates that FL-SL variants achieve competitive predictive performance and decision-facing prioritization behavior relative to standalone FL or SL, highlighting the framework's potential for real-world applications.
Demerits
Complexity and Scalability Concerns
The FL-SL framework may introduce additional complexity and overhead, particularly for large-scale deployments, which could impact its scalability and practicality in real-world settings.
Limited Generalizability to Non-Healthcare Domains
The article's focus on healthcare applications may limit the generalizability of the FL-SL framework to other domains, where different data characteristics and privacy constraints may apply.
Dependence on Specific Datasets and Evaluation Metrics
The empirical evaluation is based on a specific set of clinical datasets and evaluation metrics, which may not be representative of all possible scenarios or domains.
Expert Commentary
The FL-SL framework presents a promising approach to addressing the complex challenge of balancing predictive utility and data privacy in collaborative clinical decision support. However, further research is needed to fully explore the framework's potential, scalability, and generalizability to different domains and scenarios. Additionally, the development of new regulatory frameworks and guidelines will be crucial in ensuring the responsible and effective deployment of FL-SL systems in real-world settings.
Recommendations
- ✓ Recommendation 1: Further research is needed to investigate the FL-SL framework's performance on larger and more diverse datasets, as well as its scalability and generalizability to different domains and scenarios.
- ✓ Recommendation 2: The development of new regulatory frameworks and guidelines for data sharing and collaboration in healthcare settings will be essential in ensuring the responsible and effective deployment of FL-SL systems.
