<ul><li>The scarcity of accessible, compliant, and ethically sourced data presents a challenge for adopting AI in sensitive fields like healthcare and finance.</li><li>Diffusion models offer a solution for generating diverse synthetic data, which can be used as an alternative to restricted public datasets.</li><li>A novel federated learning framework is introduced for training diffusion models on decentralized private datasets.</li><li>The framework ensures robust differential privacy guarantees and produces high-quality samples, reducing biases and imbalances in synthetic data.</li></ul>

Personalized Federated Training of Diffusion Models with Privacy Guarantees

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