Incorporate differential privacy through custom [[Substrate]] pallets to add [[Introducing Noise|noise]] to updates, protecting against inference attacks [105]. By [[ZKP/Research/Noise/Adaptive Noise Scaling|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 [[ZKP/Research/Advanced Federated Learning/Example Application|scenarios]] where participants handle sensitive data, such as in healthcare or finance, while maintaining compatibility with Substrate's privacy-preserving mechanisms. See also: [[ZKP/Research/Advanced Federated Learning/Scalability Solutions|Scalability Solutions]]