Research efficient noise addition methods for distributed settings coordinated through [[Substrate]]'s [[Proof Pods in the Data Marketplace|off-chain workers]], ensuring scalability in [[Introducing Advanced Federated Learning|federated learning]] across multiple nodes [105]. By applying noise in a distributed manner, we reduce the computational [[ZKP/Research/Advanced Federated Learning/Secure Aggregation with SMPC|burden on any single node,]] allowing the system to scale to large networks of participants, such as in a [[ZKP/Research/Advanced Federated Learning/Scalability Solutions|global federated learning initiative]] where thousands of nodes contribute to a shared AI model, ensuring privacy without sacrificing performance while leveraging Substrate's networking capabilities. See also: [[ZKP/Research/Noise/Verification of Noise Addition|Verification of Noise Addition]]