Scalable Noise Addition
Research efficient noise addition methods for distributed settings coordinated through Substrate's off-chain workers, ensuring scalability in federated learning across multiple nodes.
Distributed Scalability for Federated Learning
By applying noise in a distributed manner, we reduce the computational burden on any single node, allowing the system to scale to large networks of participants, such as in a 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.
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