Andy Dong

Leveraging Randomness in Model and Data Partitioning for Privacy Amplification

We study how inherent randomness in the training process—where each sample (or client in federated learning) contributes only to a randomly selected portion of training—can be leveraged for privacy amplification. This includes (1) data partitioning, where a sample participates in only a subset of tr…