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Secure Multi-Modal Data Fusion in Federated Digital Health Systems via MCP

Aueaphum Aueawatthanaphisut

TL;DR

This work tackles secure, interoperable fusion of multi-modal health data across distributed, resource-constrained settings. It introduces a Model Context Protocol (MCP) as a schema-driven interoperability layer to align imaging, EMR, and IoMT data, while incorporating differential privacy, secure aggregation, and energy-aware scheduling within a unified federated optimization. The MCP-Fusion framework yields up to 9.8 percentage point improvements in diagnostic accuracy over baseline FL, reduces client dropouts by more than 50%, and maintains clinically acceptable privacy–utility trade-offs. This approach demonstrates a scalable, trustworthy pathway toward equitable next-generation federated health infrastructures that integrate heterogeneous data sources with strong privacy and efficiency guarantees.

Abstract

Secure and interoperable integration of heterogeneous medical data remains a grand challenge in digital health. Current federated learning (FL) frameworks offer privacy-preserving model training but lack standardized mechanisms to orchestrate multi-modal data fusion across distributed and resource-constrained environments. This study introduces a novel framework that leverages the Model Context Protocol (MCP) as an interoperability layer for secure, cross-agent communication in multi-modal federated healthcare systems. The proposed architecture unifies three pillars: (i) multi-modal feature alignment for clinical imaging, electronic medical records, and wearable IoT data; (ii) secure aggregation with differential privacy to protect patient-sensitive updates; and (iii) energy-aware scheduling to mitigate dropouts in mobile clients. By employing MCP as a schema-driven interface, the framework enables adaptive orchestration of AI agents and toolchains while ensuring compliance with privacy regulations. Experimental evaluation on benchmark datasets and pilot clinical cohorts demonstrates up to 9.8\% improvement in diagnostic accuracy compared with baseline FL, a 54\% reduction in client dropout rates, and clinically acceptable privacy--utility trade-offs. These results highlight MCP-enabled multi-modal fusion as a scalable and trustworthy pathway toward equitable, next-generation federated health infrastructures.

Secure Multi-Modal Data Fusion in Federated Digital Health Systems via MCP

TL;DR

This work tackles secure, interoperable fusion of multi-modal health data across distributed, resource-constrained settings. It introduces a Model Context Protocol (MCP) as a schema-driven interoperability layer to align imaging, EMR, and IoMT data, while incorporating differential privacy, secure aggregation, and energy-aware scheduling within a unified federated optimization. The MCP-Fusion framework yields up to 9.8 percentage point improvements in diagnostic accuracy over baseline FL, reduces client dropouts by more than 50%, and maintains clinically acceptable privacy–utility trade-offs. This approach demonstrates a scalable, trustworthy pathway toward equitable next-generation federated health infrastructures that integrate heterogeneous data sources with strong privacy and efficiency guarantees.

Abstract

Secure and interoperable integration of heterogeneous medical data remains a grand challenge in digital health. Current federated learning (FL) frameworks offer privacy-preserving model training but lack standardized mechanisms to orchestrate multi-modal data fusion across distributed and resource-constrained environments. This study introduces a novel framework that leverages the Model Context Protocol (MCP) as an interoperability layer for secure, cross-agent communication in multi-modal federated healthcare systems. The proposed architecture unifies three pillars: (i) multi-modal feature alignment for clinical imaging, electronic medical records, and wearable IoT data; (ii) secure aggregation with differential privacy to protect patient-sensitive updates; and (iii) energy-aware scheduling to mitigate dropouts in mobile clients. By employing MCP as a schema-driven interface, the framework enables adaptive orchestration of AI agents and toolchains while ensuring compliance with privacy regulations. Experimental evaluation on benchmark datasets and pilot clinical cohorts demonstrates up to 9.8\% improvement in diagnostic accuracy compared with baseline FL, a 54\% reduction in client dropout rates, and clinically acceptable privacy--utility trade-offs. These results highlight MCP-enabled multi-modal fusion as a scalable and trustworthy pathway toward equitable, next-generation federated health infrastructures.

Paper Structure

This paper contains 20 sections, 7 equations, 8 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Interoperability Protocols for AI Systems
  4. Federated Learning in Healthcare
  5. Multi-Modal Fusion for Clinical AI
  6. Privacy Preservation and Ethical Standards
  7. Summary of Gaps
  8. Methodology
  9. Problem Formulation
  10. Global Objective with Privacy and Energy Constraints
  11. Unified Optimization Principle
  12. Results and Analysis
  13. Overall Performance
  14. Privacy--Utility Trade-off
  15. Energy-Aware Scheduling and Stability
  16. ...and 5 more sections

Figures (8)

  • Figure 1: Comparison of (A) conventional federated learning with limited interoperability and unimodal updates and (B) the proposed MCP-enabled secure multi-modal framework integrating imaging, EMR, and IoMT data with secure aggregation, differential privacy, and energy-aware scheduling.
  • Figure 2: Framework Architecture
  • Figure 3: Detailed optimization workflow of the MCP-enabled secure multi-modal federeted fusion framework
  • Figure 4: Overall accuracy across methods. The proposed MCP-enabled framework achieved the highest accuracy due to multi-modal fusion and schema alignment.
  • Figure 5: Overall F1-score across methods. Consistent gains were observed for the proposed method under heterogeneous clients.
  • ...and 3 more figures