Differential Privacy in Federated Learning
Incorporate differential privacy through custom Substrate pallets to add noise to updates, protecting against inference attacks.
Enhanced Privacy in Sensitive Data Collaboration
By adding noise to model updates, we ensure that attackers cannot infer individual data points from the aggregated model, providing an additional layer of privacy that is particularly important in scenarios where participants handle sensitive data, such as in healthcare or finance, while maintaining compatibility with Substrate's privacy-preserving mechanisms.
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