sehamhakim/Federated_Learning_with_Blockchain_for_Distributed_Hospitals

Global Model: Decentralized Federated Learning for Distributed Hospitals

This repository hosts the global model weights aggregated during the decentralized federated learning process across distributed clinical institutions. The aggregation coordination, model integrity verification, and round participation are orchestrated via a blockchain network to ensure auditability, zero data leakage, and Byzantine fault tolerance.

Model Details

Model Description

• Model type: Biomedical Query Encoder / Text Feature Extraction
• Language(s) (NLP): English
• License: Apache-2.0
• Finetuned from model: ncbi/MedCPT-Query-Encoder
• Federated Scheme: Decentralized On-Chain Aggregation (e.g., FedAvg / Smart Contract Consensus)

Model Sources

• Repository: GitHub: Decentralized-Systems-and-Applications/Federated_Learning_with_Blockchain_for_Distributed_Hospitals
• Demo : [ Demo Video (https://drive.google.com/file/d/11C0rO3Zjp7459Yn7012KK0VBu-refau_/view)]

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Uses

Direct Use

Generating dense clinical embeddings for biomedical text retrieval, semantic document matching, and hospital query classification without exposing institutional private training data.

Downstream Use

Integrating into distributed clinical decision support systems (CDSS), electronic health record (EHR) cross-search, or zero-trust medical intelligence pipelines.

Out-of-Scope Use

• Automated diagnosis or medication prescribing without human clinician oversight.
• Handling tasks completely outside the clinical and biomedical domain without domain-specific re-evaluation.

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