Federated Unlearning
Cross-source consensus on Federated Unlearning from 1 sources and 4 claims.
1 sources · 4 claims
Uses
Comparisons
Where it comes from
Highlighted claims
- Federated unlearning removes a specific client or dataset contribution from an already trained global federated model. — Asynchronous Federated Unlearning with Invariance Calibration for Medical Imaging
- The retrained oracle is defined as a model trained from scratch on all retained datasets after removing the target client. — Asynchronous Federated Unlearning with Invariance Calibration for Medical Imaging
- The deletion request may arise from expired consent, data corrections, or privacy regulations such as GDPR and CCPA. — Asynchronous Federated Unlearning with Invariance Calibration for Medical Imaging
- Prior federated unlearning methods include partition-based, model-manipulation, and optimization-based approaches. — Asynchronous Federated Unlearning with Invariance Calibration for Medical Imaging