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A Path Integral Surrogate for Multi-Step Gradient Inversion in Federated Learning
Federated learning lets many clients train a shared model together without ever sending their private data to a central server. Each client shares only a model update, and this update should reveal far less about the client than its raw training examples would. This premise is what protects the priv
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