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Found 3 papers, 1 papers with code
Cross-silo Federated Learning with Record-level Personalized Differential Privacy
Federated learning enhanced by differential privacy has emerged as a popular approach to better safeguard the privacy of client-side data by protecting clients' contributions during the training process.
MUter: Machine Unlearning on Adversarially Trained Models
However, existing methods focus exclusively on unlearning from standard training models and do not apply to adversarial training models (ATMs) despite their popularity as effective defenses against adversarial examples.
Projected Federated Averaging with Heterogeneous Differential Privacy
The challenge is how to use such information without biasing the joint model.
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