On medical imaging federated unlearning, easy client removals make all utility-preserving methods indistinguishable, while hard class-level removals separate them, and residual membership rather than task accuracy is the main erasable signal.
FLamby: Datasets and benchmarks for cross-silo federated learning in realistic healthcare settings.Advances in Neural Information Processing Systems, 35:5315–5334, 2022
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Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging
On medical imaging federated unlearning, easy client removals make all utility-preserving methods indistinguishable, while hard class-level removals separate them, and residual membership rather than task accuracy is the main erasable signal.