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Event - Meet Us at BIO International Convention 2025
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Building Secure and Scalable AI-Driven Federated Learning Solutions for Healthcare Data
Leverage decentralized machine learning to train secure, scalable AI models without centralizing sensitive data. Our Federated Learning solution empowers healthcare to harness AI’s full potential, enabling smarter, more secure, and compliant decision-making.
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Centralized Training Limits Scalability, Privacy, and Performance
Machine learning models have traditionally relied on centralized data collection where data from multiple sources is aggregated in a central location for training. While this approach has driven many advancements, it comes with significant challenges:
Privacy, Security, and Compliance Risks – Centralizing sensitive data increases the risk of breaches and makes it harder to meet compliance standards like HIPAA and GDPR.
Data Silos – Data stored across different institutions and systems prevents a unified view, leading to incomplete insights and inconsistent models.
Bias and Poor Data Quality – Centralized models trained on small or non-representative datasets often produce biased and unreliable predictions.
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Federated Learning Architecture
Our federated learning solution is built using AWS Cloud Infrastructure with a secure and scalable architecture:
Training Setup
The system is deployed in isolated environments owned and managed by each client.
Training uses secure, access-restricted compute to ensure data remains private and compliant.
Centralized Aggregation
A central server aggregates model updates and creates a new global model.
The updated model is distributed back to clients for continued training.
Monitoring and Visualization
Training logs are captured using Amazon CloudWatch for real-time monitoring.
Results like training and validation loss, model performance, and aggregated global accuracy are visualized via the Polly Dashboard.
Secure Communication
VPC Peering allows secure, private communication between the local models and the global server.
No data or updates are transmitted over the public internet.
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How Does Federated Learning Work?
We implement multiple layers of security and privacy to ensure data remains protected.
Private Subnet for Model Training
- Model training runs inside a private subnet with no public internet access to prevent external threats.
VPC Peering for Secure Communication
- Model updates run over secure VPC connections and avoid all public network exposure.
Encryption and Least Privilege Access
- Data stored in S3 is encrypted using AWS standards.
- We follow the least privilege principle to limit access to sensitive data.
Protection Against Gradient Inversion Attacks
- Even though model updates are shared, there is still a risk of reconstructing training data through a gradient inversion attack.
Differential Privacy and Homomorphic Encryption
- Differential Privacy – Adds noise during training to protect sensitive data.
- Homomorphic Encryption – Enables secure ML by computing directly on encrypted data.
Watch Polly in Action
Watch Federated Learning in Action
Comparison of Centralized & Federated Learning
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Centralized Learning
Stores all data in one central location.
Federated Learning
Keeps data local and shares only model parameters.
Centralized Learning
Exposes raw data and increases data security risks.
Federated Learning
Protects sensitive data by never exposing raw inputs.
Centralized Learning
Struggles to meet data privacy laws like GDPR or HIPAA.
Federated Learning
Aligns with regulations using privacy-preserving machine learning techniques.
Centralized Learning
Uses narrow datasets and reduces model accuracy.
Federated Learning
Uses diverse datasets to improve model performance and fairness.
Centralized Learning
Relies on centralized infrastructure.
Federated Learning
Scales across decentralized systems with no need to move data.
Key Data Quality Dimensions
Electronic Medical Records (EMRs)
Hospitals use federated learning (FL) to predict diseases, mortality, and treatment outcomes without moving sensitive EMR data. FL models outperform local ones, especially for ICU and cardiac patients. For example, a study on ICU patients aged 20-40 shows a 10% accuracy boost over RNNs using tensor factorization and graph-based attention.
Clinical Predictions
FL supports early disease detection and risk scoring by learning from patient data across multiple institutions. It ensures strong predictive performance while complying with healthcare data privacy regulations.
Smart Grid Management
FL helps build predictive models for energy consumption and load balancing by allowing different grid entities to train on local data and share only model updates. This improves grid efficiency without compromising data privacy.
Cybersecurity
Organizations use Federated Learning (FL) to collaboratively train threat detection models on internal data without sharing raw information. This boosts security while keeping proprietary data private.
Medical Imaging
FL helps in analyzing MRIs and other medical scans from multiple centers. Techniques like Federated Principal Component Analysis (FPCA) enable hospitals to extract useful features from images while ensuring that raw data remains secure and decentralized.
EEG Signal Classification
Advanced FL models such as HHHFL classify EEG signals with high accuracy, even when labeled data is limited. This enables privacy-preserving neural signal analysis without compromising model performance.
Edge Computing
and IoT
FL enables devices at the edge of the network, such as smartphones and IoT devices to collaboratively train shared models without centralizing data. This approach enhances privacy and significantly reduces communication overhead.
Healthcare IT Systems
Hospitals and clinics use FL to build diagnostic models from patient data stored locally. By exchanging only model parameters, they enhance accuracy without risking patient privacy.
Trusted by the World's Leading Biopharma R&D Teams
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info@elucidata.io
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