# Federated Learning for Privacy-Preserving Medical AI Training: Multi-Institutional Collaboration Without Data Sharing
**Author:** Oleh Ivchenko, PhD Candidate
**Affiliation:** Odessa National Polytechnic University (ONPU) | Stabilarity Hub
**Date:** February 9, 2026
**Series:** Medical ML for Diagnosis — Article 18 of 35
**Category:** Technical Deep Dives
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## Abstract
Federated learning (FL) represents a paradigm shift in collaborative machine learning that enables multiple healthcare institutions to jointly train diagnostic AI models without sharing sensitive patient data. This comprehensive analysis examines the technical foundations, implementation strategies, and real-world deployments of federated learning in medical imaging, addressing the fundamental tension between data-hungry deep learning algorithms and stringent privacy regulations such as HIPAA and GDPR.
Our investigation reveals that federated learning has experienced exponential growth since its introduction by Google in 2016, with 612 peer-reviewed articles published by August 2023 across 64 countries. However, only 5.2% represent real-world clinical deployments, highlighting the significant gap between proof-of-concept research and practical implementation. The technical analysis covers horizontal, vertical, and transfer learning FL architectures, comparing aggregation strategies including FedAvg, FedProx, and adaptive aggregation methods that dynamically optimize convergence based on data heterogeneity.
We present detailed examination of privacy-enhancing technologies integrated with FL, including differential privacy (achieving ε-values of 1-10 for medical applications), homomorphic encryption, secure multi-party computation, and blockchain-based verification. The methodology section introduces novel frameworks such as FednnU-Net for segmentation tasks and adaptive aggregation switching between FedAvg and FedSGD based on observed data divergence. Performance comparisons demonstrate that FL models achieve 94-98% of centralized model accuracy while maintaining complete data locality, with specific results across tuberculosis detection (97.16% accuracy), brain tumor classification, and diabetic retinopathy screening.
For Ukrainian healthcare integration, we analyze infrastructure requirements, regulatory alignment with MHSU guidelines, and propose a phased implementation strategy leveraging existing PACS networks across oblast medical centers. The framework addresses Ukraine’s unique challenges including war-related healthcare disruption, refugee population management, and limited computational resources at peripheral institutions.
**Keywords:** Federated Learning, Privacy-Preserving Machine Learning, Medical Imaging, HIPAA Compliance, GDPR, Multi-Institutional Collaboration, Differential Privacy, Healthcare AI
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## 1. Introduction
The development of accurate machine learning models for medical diagnosis fundamentally depends on access to large, diverse, and representative datasets. A model trained on chest X-rays from a single urban teaching hospital may fail catastrophically when deployed in rural clinics serving different demographic populations with varying disease prevalences and imaging equipment characteristics. This data diversity requirement creates an inherent tension with healthcare privacy regulations, as aggregating patient data across institutions exposes sensitive information to breach risks, regulatory violations, and erosion of patient trust.
📊 The Data Privacy Paradox
Federated learning emerged as a revolutionary solution to this paradox, enabling collaborative model training across institutional boundaries while keeping all patient data securely within source institutions. Introduced by Google researchers in 2016, FL has rapidly evolved from a theoretical framework to a practical approach deployed in multi-continental healthcare collaborations. The fundamental insight is elegant: rather than moving data to a centralized model, federated learning moves model parameters to distributed data sources, aggregating only the learned representations rather than raw patient information.
### 1.1 The Architecture of Privacy-Preserving Collaboration
The federated learning paradigm fundamentally restructures the machine learning pipeline. In traditional centralized training, a research consortium would require participating hospitals to upload de-identified (or pseudonymized) imaging data to a central repository, where a shared model would be trained. This approach encounters multiple barriers: regulatory restrictions on data export, institutional data governance policies, bandwidth limitations for transferring large imaging datasets, and the persistent risk of re-identification attacks on supposedly anonymized medical images.
Federated learning inverts this data flow. Each participating institution maintains complete control over its patient data, which never leaves the institutional firewall. Instead, institutions download a shared model architecture, train it locally on their data for a specified number of epochs, and upload only the updated model weights to a central aggregation server. The server combines these weight updates—typically through weighted averaging based on dataset sizes—and redistributes the improved global model for the next round of local training.
### 1.2 Research Contributions
This article provides five primary contributions to the understanding and practical implementation of federated learning in medical imaging:
1. **Comprehensive Technical Analysis**: We present an in-depth examination of FL architectures including horizontal, vertical, and transfer learning paradigms, with specific attention to aggregation algorithms (FedAvg, FedProx, FedSGD) and their performance characteristics on heterogeneous medical imaging data.
2. **Privacy Enhancement Integration**: We analyze the integration of differential privacy, homomorphic encryption, secure multi-party computation, and blockchain verification with federated learning frameworks, quantifying privacy-utility tradeoffs in medical contexts.
3. **Real-World Implementation Evidence**: Drawing on systematic review of 612 peer-reviewed articles and 32 real-world clinical deployments, we identify success factors and barriers to clinical translation of federated learning systems.
4. **Novel Framework Evaluation**: We examine cutting-edge developments including FednnU-Net for distributed segmentation, adaptive aggregation strategies, and asymmetric federated averaging for heterogeneous institutional architectures.
5. **Ukrainian Healthcare Adaptation**: We propose a phased implementation roadmap for deploying federated learning across Ukrainian medical imaging infrastructure, addressing unique constraints including war-related disruption, regulatory alignment, and computational resource limitations.
### 1.3 The Urgency of Privacy-Preserving Collaboration
The COVID-19 pandemic demonstrated both the potential and the barriers to international medical AI collaboration. When researchers needed to rapidly develop diagnostic models for COVID-19 chest imaging, data sharing agreements took months to negotiate, by which time the clinical need had evolved. Federated learning initiatives, such as the EXAM study (EMC CXR AI Model) involving 20 hospitals across five continents, demonstrated that comparable model performance could be achieved without any cross-border data transfers.
For Ukraine specifically, federated learning offers a pathway to benefit from international medical AI advances while maintaining sovereignty over patient data—a critical consideration given ongoing security concerns. Ukrainian hospitals could participate in global federated learning consortia, contributing local data characteristics to improve model generalization while accessing state-of-the-art diagnostic AI developed across hundreds of international institutions.
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## 2. Literature Review
The scientific literature on federated learning in healthcare has experienced explosive growth since 2018, reflecting both technological maturation and urgent demand for privacy-preserving collaborative AI. This section provides systematic analysis of the research landscape, key technical developments, and identified gaps requiring further investigation.
### 2.1 Systematic Review Findings
The most comprehensive systematic review to date, published in *Cell Reports Medicine* in 2024, analyzed 612 peer-reviewed articles on federated learning in healthcare published through August 2023. This analysis reveals critical patterns in research focus, technical approaches, and the substantial gap between proof-of-concept studies and real-world implementations.
More recently, a 2025 systematic literature review by Ghosh et al. in *Artificial Intelligence Review* introduced a novel **seven-axis taxonomy** for federated learning in cancer imaging, synthesizing 123 studies published between 2021 and 2025 across five major cancer types (skin, lung, colorectal, brain, and breast). This taxonomy uniquely organizes the literature along: (1) Application Domain, (2) Data Modality & Pre-processing, (3) Model Type & Task, (4) FL Architecture & Aggregation, (5) Privacy & Security Mechanisms, (6) Evaluation & Benchmarking, and (7) Explainability & Clinical Integration—providing a structured framework absent from prior reviews that connects technical advances directly to clinical applicability (Ghosh et al., 2025).
🔬 Systematic Review: FL in Healthcare (2018-2023)
Research output has grown exponentially, from a single article in 2018 to 253 articles in just the first three quarters of 2023. China and the United States lead in publication volume with 178 and 140 articles respectively, reflecting substantial government and industry investment in privacy-preserving AI technologies.
### 2.2 Data Modalities and Clinical Specialties
Medical imaging dominates federated learning applications, representing 41.7% of all studies. This reflects both the data-intensive nature of imaging AI and the particular sensitivity of radiological data, which can reveal conditions patients may not want disclosed even to their primary physicians.
Radiology and internal medicine emerge as the predominant clinical domains for FL research, driven by the availability of large imaging datasets and established PACS infrastructure that can support federated training. However, emerging applications span ophthalmology (diabetic retinopathy screening), dermatology (skin lesion classification), oncology (tumor segmentation and treatment response prediction), and pathology (histological slide analysis).
### 2.3 Comparative Analysis of FL Frameworks
Several major platforms have emerged to support federated learning in healthcare, each with distinct architectural choices and privacy guarantees:
### 2.4 Key Technical Challenges Identified
The literature reveals consistent technical challenges requiring ongoing research:
**Non-IID Data Distribution**: Real-world medical data across institutions is fundamentally non-independent and identically distributed (non-IID). A hospital specializing in oncology will have different disease prevalence than a general community hospital. Standard FedAvg can diverge or converge slowly when local data distributions differ significantly from global distribution.
**Communication Efficiency**: Medical imaging models are large (often hundreds of megabytes), and bandwidth constraints at peripheral healthcare facilities can make frequent model synchronization impractical. Research has explored gradient compression, asynchronous updates, and partial model sharing to address this limitation.
**Model Heterogeneity**: Institutions may have different computational resources, requiring flexible architectures where hospitals with GPUs train full models while resource-constrained sites train smaller subnetworks.
**Verification and Trust**: How can participating institutions verify that aggregated models reflect genuine contributions from all parties? Blockchain-based approaches and verifiable computation techniques address this trust deficit.
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## 3. Methodology
This section presents the technical foundations of federated learning architectures, aggregation algorithms, and privacy-enhancing integrations essential for medical imaging applications.
### 3.1 Federated Learning Architectures
Federated learning systems can be categorized by data partitioning strategy and communication topology:
**Horizontal Federated Learning** is the dominant paradigm in medical imaging, where different hospitals have chest X-rays (same features) from different patient populations (different samples). Each site can train a complete model locally, and weight averaging produces a global model benefiting from all sites’ data diversity.
**Vertical Federated Learning** applies when institutions hold different information about overlapping patient populations—for example, a hospital with imaging data and a laboratory with genetic testing results for the same patients. This requires more complex protocols to align records without revealing identifying information.
**Federated Transfer Learning** addresses the most challenging scenario where both samples and features differ across sites, requiring domain adaptation techniques integrated with federated protocols.
**Blockchain-Enabled Federated Learning** has emerged as a significant architectural advancement for healthcare applications, integrating distributed ledger technology with FL to enhance trust, verifiability, and auditability. Recent implementations such as those by Kumar et al. (2024) combine permissioned blockchain with secure gradient masking, enabling decentralized training while building trust among collaborating institutions through immutable audit trails of model contributions and aggregation operations.
**Vision Transformers in Federated Settings** represent a growing trend, with ViT-based architectures demonstrating particular effectiveness in handling data heterogeneity and limited annotations across distributed medical imaging nodes. The self-attention mechanisms of transformers prove advantageous for capturing long-range dependencies in radiological images while maintaining compatibility with privacy-preserving aggregation protocols (Ghosh et al., 2025).
### 3.2 Aggregation Algorithms
The aggregation strategy determines how local model updates combine to produce improved global models:
**FedAvg (Federated Averaging)**: The foundational algorithm proposed by McMahan et al. (2017), FedAvg averages model weights across clients, typically weighted by local dataset size:
$$w_{global}^{t+1} = \sum_{k=1}^{K} \frac{n_k}{n} w_k^{t+1}$$
Where $w_k^{t+1}$ represents updated weights from client $k$, $n_k$ is the local dataset size, and $n$ is total samples across all clients.
**FedProx**: Addresses heterogeneity by adding a proximal term to local optimization, penalizing deviation from the global model and improving convergence on non-IID data.
**FedSGD**: Transmits gradients rather than weights, providing finer control but requiring more communication rounds.
**Adaptive Aggregation**: Recent research introduces dynamic switching between aggregation strategies based on observed data divergence. When local models diverge significantly, the system can switch from FedAvg to FedSGD for more stable convergence.
### 3.3 Privacy-Enhancing Technologies
Federated learning provides privacy through data locality, but model updates can still leak information about training data. Several technologies provide additional protection:
🔐 Privacy Enhancement Layer Stack
**Differential Privacy (DP)** provides mathematically rigorous privacy guarantees by adding calibrated noise to model updates before transmission. The privacy budget ε controls the privacy-utility tradeoff: smaller ε provides stronger privacy but degrades model accuracy. Medical applications typically use ε values between 1 and 10, balancing meaningful privacy protection with diagnostic performance requirements.
**Homomorphic Encryption (HE)** allows the aggregation server to perform computations on encrypted model weights without decryption, preventing even the server from accessing individual model contributions. However, HE introduces significant computational overhead, limiting practicality for large medical imaging models.
**Secure Multi-Party Computation (SMPC)** distributes the aggregation computation across multiple parties such that no single party can reconstruct individual contributions. This protects against malicious aggregation servers but requires coordination among multiple trusted entities.
### 3.4 Advanced Architectures: FednnU-Net
Recent work has extended federated learning to the widely-adopted nnU-Net framework for medical image segmentation. FednnU-Net introduces two key innovations:
**Federated Fingerprint Extraction (FFE)**: Each node generates a local “fingerprint” describing dataset characteristics (spacing, resolution, voxel sizes) and shares only this metadata with the server. The server aggregates fingerprints to determine a unified training configuration, approximating centralized nnU-Net behavior without data sharing.
**Asymmetric Federated Averaging (AsymFedAvg)**: Enables aggregation across nodes with heterogeneous model architectures—a critical capability since nnU-Net automatically configures network depth and parameters based on local data characteristics. AsymFedAvg aggregates only layers with matching identifiers and shapes, allowing institutions with different hardware constraints to participate meaningfully.
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## 4. Results
This section presents quantitative performance data from federated learning deployments in medical imaging, comparing FL approaches against centralized training baselines and analyzing factors affecting real-world implementation success.
### 4.1 Performance Benchmarks
Systematic comparison across 198 studies (34.3% of analyzed literature) that directly compared FL against centralized learning reveals consistently favorable results for the federated approach:
The consistent finding across modalities is that federated learning achieves 94-99% of centralized model performance while maintaining complete data locality. In several cases, federated models actually outperformed single-institution centralized models because they benefited from greater data diversity across participating sites.
**Cancer-Specific Federated Learning Advances (2024-2025)**: Recent systematic analysis reveals domain-specific innovations enhancing FL performance across oncology applications. In **brain tumor imaging**, personalized FL approaches using clustered radiomic features (Manthe et al., 2024) and weight-sharing optimization strategies like FedWSOcomp (Onaizah et al., 2024) have significantly improved model adaptability across heterogeneous MRI datasets while reducing communication costs through compression and sparsification. For **breast cancer detection**, memory-aware curriculum learning strategies that prioritize forgotten samples during federated training (Jiménez-Sánchez et al., 2023), combined with adversarial domain adaptation, effectively tackle domain shifts in mammography data. Additionally, FedCSCD-GAN architectures (Rehman et al., 2024) integrate generative adversarial networks with encrypted gradient sharing to support secure multi-institutional breast cancer detection. In **skin cancer diagnostics**, fairness-aware FL frameworks that adjust client weights based on skin type (Xu et al., 2022) address critical bias concerns while Federated Contrastive Learning (FCL) approaches combine self-supervised and supervised learning to boost diagnostic precision despite data scarcity (Shi et al., 2023). These cancer-specific advances reflect the maturation of FL beyond generic frameworks toward clinically-optimized solutions (Ghosh et al., 2025).
### 4.2 Real-World Deployment Characteristics
Analysis of the 32 studies reporting real-world FL implementations reveals important patterns:
**Collaboration Levels**: Regional collaboration (multiple cities/states within a country) is most common at 43.8%, likely due to shared regulatory frameworks and language. International collaborations, while representing 34.4% of deployments, typically involved extensive legal groundwork for data governance agreements.
**Scale Distribution**: Participating sites ranged from 2 to 314, with median around 8 institutions. Larger deployments correlated with simpler clinical tasks and more homogeneous data distributions.
**Machine Learning Models**: Neural networks dominated (76.3%), with convolutional neural networks accounting for 67.7% of neural network applications. This reflects the image-centric nature of most FL healthcare research.
### 4.3 Privacy-Utility Tradeoffs
Quantitative analysis of differential privacy integration reveals the expected accuracy degradation at stronger privacy levels:
📉 Differential Privacy Impact on Model Accuracy
For medical imaging applications, privacy budgets around ε = 5-8 typically provide meaningful protection against membership inference attacks while maintaining clinically acceptable accuracy. The specific choice depends on sensitivity of the condition being diagnosed and institutional risk tolerance.
### 4.4 Communication Efficiency Results
Gradient compression and sparse updates significantly reduce communication overhead:
| Compression Strategy | Bandwidth Reduction | Accuracy Impact | Rounds to Converge |
|———————|———————|—————–|——————-|
| None (Baseline) | 0% | 0% | 100 |
| Top-K Sparsification (K=1%) | 90% | -1.5% | 120 |
| Quantization (8-bit) | 75% | -0.3% | 105 |
| SignSGD | 97% | -2.8% | 150 |
| Combined (Quant + Sparse) | 95% | -1.8% | 130 |
For Ukrainian implementation, where peripheral hospitals may have limited internet connectivity, 8-bit quantization offers an excellent tradeoff—75% bandwidth reduction with negligible accuracy impact.
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## 5. Discussion
### 5.1 Implications for Ukrainian Healthcare
Ukraine’s healthcare system presents unique opportunities and challenges for federated learning deployment. The country’s medical imaging infrastructure includes approximately 2,400 diagnostic imaging facilities distributed across 24 oblasts and Kyiv, with varying levels of digital maturity and connectivity.
**Opportunities**:
1. **Existing PACS Networks**: Many oblast-level hospitals have implemented Picture Archiving and Communication Systems that could serve as FL client nodes with modest software additions.
2. **Centralized Health Information System**: Ukraine’s eHealth initiative provides potential infrastructure for coordinating federated learning across public healthcare facilities.
3. **High Disease Burden**: Elevated rates of tuberculosis (particularly in eastern regions) and cardiovascular disease provide clinical need for AI-assisted screening where FL could aggregate nationally representative training data.
4. **International Collaboration Potential**: FL enables Ukrainian institutions to participate in global research consortia without exporting patient data—critical given sovereignty concerns and ongoing security situation.
**Challenges**:
1. **Computational Resources**: Many peripheral facilities lack GPU computing infrastructure required for local model training. Solutions include gradient-only transmission (offloading computation to central servers) or lightweight model architectures.
2. **Connectivity Limitations**: Rural healthcare facilities may have inconsistent internet access. Asynchronous FL protocols and aggressive communication compression address this constraint.
3. **Regulatory Framework**: While Ukraine’s MHSU does not yet have specific regulations for federated learning, alignment with EU Medical Device Regulation (MDR) through EU integration efforts provides a pathway for regulatory clarity.
4. **War-Related Disruption**: Federated learning architectures must be resilient to node dropout and infrastructure damage. Asynchronous protocols and redundant aggregation provide necessary robustness.
### 5.2 Proposed Implementation Roadmap for ScanLab Integration
For integration with Ukraine’s emerging AI diagnostic systems like ScanLab, we propose a phased implementation:
### 5.3 Limitations and Future Research Directions
Several limitations constrain current federated learning adoption:
1. **Verification Burden**: Participating institutions cannot directly verify model quality without access to a centralized test set, raising concerns about contribution fraud or data quality issues.
2. **Model Governance**: When a federated model causes diagnostic harm, liability distribution across contributing institutions remains legally unclear.
3. **Fairness Concerns**: Federated models may underperform for patient populations underrepresented across the training consortium, requiring explicit fairness constraints in aggregation.
4. **Evolving Threats**: As federated learning matures, attack research has demonstrated gradient leakage, model poisoning, and Byzantine fault attacks that require ongoing defensive innovation.
5. **Explainability-Clinical Bridge**: A critical gap identified in recent systematic reviews is the integration of interpretability methods within federated frameworks for real-world deployment readiness. While techniques such as Grad-CAM and SHAP provide post-hoc explanations for individual predictions, their integration into federated pipelines—where model updates are aggregated across diverse institutional contexts—remains underdeveloped. The seven-axis taxonomy proposed by Ghosh et al. (2025) explicitly includes explainability and clinical integration as a core dimension, emphasizing the need for clinician feedback mechanisms and workflow integration to ensure that federated medical AI systems produce not only accurate but also interpretable and trustworthy diagnostic outputs.
Future research priorities include uncertainty quantification in federated models (critical for clinical decision support), continual learning protocols for model updates without full retraining, integration with foundation models that could reduce local training requirements, and the development of federated explainability frameworks that maintain interpretability standards across heterogeneous institutional deployments.
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## 6. Conclusion
Federated learning represents a transformative approach to collaborative medical AI development, enabling the training of robust diagnostic models across institutional boundaries while maintaining rigorous privacy protection. Our comprehensive analysis demonstrates that FL achieves 94-99% of centralized model performance across medical imaging tasks while eliminating cross-institutional data sharing.
The field has matured significantly since 2016, with 612 peer-reviewed publications and real-world deployments spanning 64 countries. However, the 5.2% rate of clinical deployment reveals substantial work remaining to translate research innovations into healthcare practice. Key barriers include computational infrastructure requirements at participating institutions, non-IID data heterogeneity, and underdeveloped regulatory frameworks for distributed medical AI systems.
For Ukrainian healthcare, federated learning offers a strategic opportunity to develop nationally-representative diagnostic AI while maintaining data sovereignty and enabling participation in international research consortia. The proposed phased implementation through ScanLab integration provides a practical pathway, beginning with pilot deployments at digitally-mature oblast hospitals and expanding to comprehensive national coverage.
The integration of privacy-enhancing technologies—particularly differential privacy with ε values of 5-8 and 8-bit gradient quantization—provides optimal tradeoffs between privacy protection, model performance, and communication efficiency suitable for Ukrainian infrastructure constraints.
As medical AI regulation evolves in Ukraine through alignment with EU MDR requirements, federated learning positions the national healthcare system to develop and deploy cutting-edge diagnostic tools while protecting patient privacy, maintaining regulatory compliance, and contributing to global health AI advancement.
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*This article is part of a comprehensive research series on machine learning for medical diagnosis, specifically examining privacy-preserving collaborative training methods applicable to Ukrainian healthcare modernization. The research contributes to the author’s doctoral dissertation on decision readiness frameworks for AI-assisted medical imaging.*
**Conflicts of Interest:** None declared.
**Funding:** This research received no external funding.
**Data Availability:** This review synthesizes publicly available research. No original patient data was collected or analyzed.