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Ensuring Data Privacy in FL

Intelligence,
Decentralized.

An interactive dashboard for a privacy-preserving federated learning system in healthcare.

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The model comes to the data. Not the other way around.

Federated Learning distributes model training to local devices, ensuring raw, sensitive data never needs to be centralized. It's the foundation of collaborative intelligence without compromise.

Making individuals mathematically invisible.

By injecting calibrated noise into the training updates, Differential Privacy provides a formal guarantee that the model's output cannot be used to infer information about any single individual in the dataset.

Live Control Panel

Monitor and control the federated training process in real-time.

Accuracy

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Loss

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Epsilon (Ξ΅)

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Ready to start a new training run.

The Tech Stack

Drag to explore the cloud and system components.

Amazon EC2

Runs the core FL server, clients, and API on scalable virtual compute instances.

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Amazon S3

Provides durable object storage for metrics and hosts the static frontend application globally.

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Amazon KMS

Manages cryptographic keys to provide an auditable layer of security for sensitive metrics.

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AWS IAM

Ensures secure, role-based access between all AWS services, eliminating the need for static credentials.

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Flower

A flexible federated learning framework that orchestrates the entire distributed training process.

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Opacus

A PyTorch library that enables training with Differential Privacy with minimal code changes.

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This is the blueprint for trustworthy AI.

By combining federated learning, differential privacy, and a robust cloud architecture, we've built more than a demoβ€”we've built a vision for the future of intelligent, privacy-first systems.