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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Privacy Enhancing and Scalable Federated Learning to Accelerate AI Implementation in Cross-Silo and IoMT

Siddartha Rachakonda, Shiva Moorthy, Anshul Jain

IEEE Journal of Biomedical and Health Informatics

|June 22, 2022

Summary

Federated Learning (FL) enables collaborative machine learning without data movement. This novel framework enhances scalability, security, and privacy, integrating multi-party computation for robust protection in diverse applications like IoMT.

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Area of Science:

Machine Learning
Artificial Intelligence
Data Privacy

Background:

Federated Learning (FL) allows collaborative model training on decentralized data, preserving privacy and data access rights.
Existing FL frameworks face challenges in scalability, security, aggregation, provenance, and production readiness.

Purpose of the Study:

To propose a novel Federated Learning framework addressing current limitations.
To enhance scalability, monitoring, privacy, and use-case support in FL.
To integrate multi-party computation (MPC) for enhanced security against reverse engineering.

Main Methods:

Developed a novel FL framework with scalable processing for data, devices, sites, and collaborators.
Integrated multi-party computation (MPC) into the FL architecture.
Evaluated the framework in cross-device and cross-silo settings, including an AI-driven Internet of Medical Things (IoMT) environment.

Main Results:

The proposed framework demonstrates enhanced scalability and monitoring capabilities.
Integration of MPC effectively prevents reverse engineering attacks.
Successful application in diverse use cases, including IoMT, showcasing suitability for various AI techniques.

Conclusions:

The novel FL framework offers a scalable, secure, and production-ready solution for decentralized machine learning.
The framework's integration with MPC significantly bolsters data security.
Demonstrated feasibility for efficient FL implementation in clinical settings and IoMT environments.