The content discusses the protection mechanisms, trade-offs between privacy and utility, and algorithms for achieving near-optimal utility in privacy-preserving federated learning. It emphasizes the importance of balancing privacy requirements with maintaining high model utility.
Federated learning enables collaborative model building without sharing private data. Protection mechanisms distort model parameters to ensure privacy while maintaining utility. The content explores upper bounds for utility loss and trade-offs between privacy leakage and utility. Algorithms are proposed to achieve near-optimal utility while meeting privacy requirements.
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