A federated multi-model data processing system for heterogeneous healthcare environments

Abstract

A federated multimodal data processing system (100) for heterogeneous healthcare environments, comprising: a multitude of participant nodes, each comprising local health data repositories containing multimodal data selected from electronic health records, medical images, clinical texts, laboratory data, and physiological sensor data; a set of modality-specific machine learning models that are executed at each participant node to process the respective data modalities locally and generate protected model updates or latent feature representations; an orchestration coordinator configured to manage federated training rounds, distribute global model parameters, select participant nodes, and aggregate received updates; a data protection and security module configured to use encryption, secure aggregation, differential privacy and authentication mechanisms to prevent the disclosure of raw patient data; a multi-model fusion layer configured to integrate heterogeneous model outputs using attention-based weighting, ensemble learning, or knowledge distillation to produce unified federated model artifacts; and a governance and audit module configured to record consent information, data origin, model versions, and training activities to ensure compliance with legal regulations, This enables collaborative learning across distributed institutions without the need to transfer raw healthcare data.

Description

[0001] The present invention relates generally to systems for processing health data and artificial intelligence. In particular, the invention relates to a federated multi-model system for the distributed, privacy-compliant analysis of heterogeneous health data, including electronic health records, medical imaging, clinical texts, and physiological sensor data.

[0002] Modern healthcare systems generate vast amounts of data from diverse sources, including electronic health records, medical imaging procedures, laboratory reports, genomic information, clinical reports, and continuous data streams from wearable and bedside monitoring devices. These data sources vary considerably in structure, scope, format, and semantic meaning, resulting in heterogeneous datasets that are difficult to integrate and analyze using traditional centralized data management and machine learning approaches. The effective utilization of such multimodal health data is critical for improving diagnostic accuracy, personalized treatment planning, disease prediction, and operational efficiency in healthcare settings.Traditionally, healthcare analytics platforms rely on centralized data aggregation, where data from multiple hospitals or clinics are transferred to a single repository for processing and model training. However, this approach presents significant challenges, including the risk of data breaches, violations of patient privacy, regulatory constraints such as data localization laws and healthcare compliance frameworks, and high infrastructure costs for storing and processing large volumes of medical data. Furthermore, centralized models often struggle to adapt to institutional differences in patient populations, clinical practices, and data quality, leading to reduced generalizability and biased results.Existing federated learning techniques have proven to be a partial solution, as they enable distributed model training without sharing raw data. However, most current federated systems are designed for homogeneous data types and single-model architectures, such as uniform neural networks trained on similar datasets. In healthcare, on the other hand, there are multiple data modalities that require specialized models, including convolutional neural networks for image data, natural language processing models for clinical notes, and structured learning algorithms for tabular datasets. Traditional federated approaches lack efficient mechanisms for coordinating these heterogeneous models, fusing multimodal knowledge, and managing statistical heterogeneity across different institutions.Furthermore, existing systems offer only limited support for adaptive participation, personalized model updates, mechanisms to enhance data privacy beyond basic parameter averaging, and governance features such as auditability, consent management, and regulatory traceability. These limitations hinder the deployment of scalable, trustworthy artificial intelligence solutions in real-world healthcare networks, where legal accountability and clinical reliability are critical. Accordingly, there is a need for an advanced system that can securely and efficiently integrate heterogeneous health data across decentralized environments, coordinate multiple modality-specific learning models, protect patient privacy, and provide robust, compliant, and scalable analytics capabilities.The present invention addresses these challenges by introducing a federated multi-model system specifically designed for distributed fusion and intelligent processing of health data.

[0003] To solve this problem, the present invention offers a federated multi-model data processing system for heterogeneous healthcare environments.

[0004] The system enables the collaborative training of modality-specific machine learning models, including imaging models, clinical text models, tabular data models and physiological signal models, while respecting data privacy and complying with legal regulations.

[0005] The system integrates multimodal health data through a unified latent representation and an intelligent model fusion mechanism to improve diagnostic accuracy and predictive performance.

[0006] The system implements privacy protection techniques such as secure aggregation, differential privacy, encryption, and trusted execution environments to protect sensitive medical information during federated learning processes.

[0007] The system supports adaptive participation of health nodes, personalized model updates, and efficient use of computing and network resources.

[0008] The system ensures governance, auditability and compliance by maintaining detailed records of consents, data origin, model versions and training activities.

[0009] The system improves robustness against data heterogeneity and institutional variability through the use of attention-based fusion, ensemble learning, and knowledge distillation strategies.

[0010] The system reduces the risks associated with centralized data storage, including security breaches, regulatory violations, and operational inefficiencies.

[0011] The present invention relates to a federated multi-model system for the distributed, privacy-compliant processing and analysis of heterogeneous health data across multiple decentralized healthcare facilities. The system comprises a plurality of participant nodes, each configured to host modality-specific machine learning models for processing different data types, including medical imaging, electronic health records, clinical texts, laboratory data, and physiological sensor signals, as well as a central coordinator configured to manage federated training rounds, model synchronization, and resource allocation. Each participant node processes health data locally using one or more specialized models and generates privacy-protected model updates or latent representations without disclosing the raw patient data.A data protection and security module employs techniques such as secure aggregation, differential privacy, encryption, and attestation to ensure confidentiality and regulatory compliance during distributed learning processes. The system further includes a multi-model fusion layer that integrates heterogeneous model outputs using attention mechanisms, ensemble learning, or knowledge distillation to generate unified federated model artifacts or personalized institutional models. The orchestration coordinator dynamically schedules participating nodes based on compute capacity, network conditions, and data relevance, enabling scalable and adaptive collaboration across various healthcare environments.Governance and audit modules maintain records of consent, data origin, training activities, and model versions to ensure transparency and compliance with health regulations. By enabling collaborative intelligence across various data modalities while maintaining rigorous data privacy controls, the invention overcomes the limitations of centralized analytics and traditional federated learning systems. The disclosed system improves diagnostic accuracy, predictive health analytics, interoperability, and robustness to statistical heterogeneity, providing a secure, scalable, and efficient framework for deploying next-generation artificial intelligence in healthcare.

[0012] The present invention discloses a federated multi-model system (100) for the secure and distributed processing of heterogeneous health data across decentralized medical facilities, wherein a plurality of participant nodes locally store various health datasets, including electronic health records, medical imaging, clinical texts, laboratory information and physiological sensor data, and each node executes modality-specific machine learning models optimized for the respective data types to generate local model updates or latent representations without transmitting raw patient data;A coordinator manages federated training cycles by scheduling participant nodes, distributing global model configurations, and aggregating received updates via secure communication channels, while a data protection and security module ensures confidentiality through secure aggregation, encryption, differential privacy, and authentication mechanisms; a multimodal fusion layer integrates heterogeneous outputs using attention-based weighting, ensemble learning, latent spatial alignment, or knowledge distillation to generate unified federated model artifacts or personalized institutional models;and a governance and audit module records consent metadata, data origin, model versions and training activities to ensure regulatory compliance and transparency, thereby enabling (100) collaborative intelligence in distributed healthcare environments, improving multimodal analytics performance, reducing data silos and privacy risks, and providing a scalable, interoperable and robust artificial intelligence infrastructure suitable for real-world healthcare ecosystems.

Claims

[1] A federated multimodal data processing system (100) for heterogeneous health environments, comprising: a multitude of participant nodes, each comprising local health data repositories containing multimodal data selected from electronic health records, medical images, clinical texts, laboratory data, and physiological sensor data; a set of modality-specific machine learning models that are executed at each participant node to process the respective data modalities locally and generate protected model updates or latent feature representations; an orchestration coordinator configured to manage federated training rounds, distribute global model parameters, select participant nodes, and aggregate received updates; a data protection and security module configured to use encryption, secure aggregation, differential privacy and authentication mechanisms to prevent the disclosure of raw patient data; a multi-model fusion layer configured to integrate heterogeneous model outputs using attention-based weighting, ensemble learning, or knowledge distillation to produce unified federated model artifacts; and a governance and audit module configured to record consent information, data origin, model versions, and training activities to ensure compliance with legal regulations, This enables collaborative learning across distributed institutions without the need to transfer raw healthcare data. [2] System (100) according to claim 1, wherein the modality-specific machine learning models comprise convolutional neural networks for medical imaging, natural language processing models for clinical texts, decision tree-based models for structured data sets and temporal neural networks for physiological time series data. [3] System (100) according to claim 1, wherein the orchestration coordinator dynamically selects participant nodes based on computing resources, network availability and data relevance. [4] System (100) according to claim 1, wherein the data protection and security module implements trusted execution environments or cryptographic protocols to verify secure participation. [5] System (100) according to claim 1, wherein the multi-model fusion layer aligns heterogeneous latent representations prior to aggregation in a common embedding space. [6] System (100) according to claim 1, wherein the fusion layer generates either a global unified model or personalized institutional models. [7] System (100) according to claim 1, wherein differential privacy noise is applied adaptively based on data sensitivity levels. [8] System (100) according to claim 1, wherein the governance and audit module maintains immutable logs of the federated learning processes. [9] System (100) according to claim 1, further comprising a resource-conscious planning module for optimizing communication and computing efficiency.

Images