Proves convergent privacy bounds for Noisy-FedAvg and stable lower bounds for Noisy-FedProx in FL-DP via f-DP and shifted interpolation, replacing divergent composition bounds.
Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated 35 Y
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Convergent Differential Privacy Analysis for General Federated Learning
Proves convergent privacy bounds for Noisy-FedAvg and stable lower bounds for Noisy-FedProx in FL-DP via f-DP and shifted interpolation, replacing divergent composition bounds.