ManiF-SMC uses manifold representation forgetting with self-mode connectivity to achieve approximate unlearning comparable to SOTA methods on four datasets while staying in representation space.
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A survey organizing knowledge distillation techniques for addressing privacy, heterogeneity, communication, and personalization challenges in federated learning.
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Approximate Machine Unlearning through Manifold Representation Forgetting Guided by Self Mode Connectivity
ManiF-SMC uses manifold representation forgetting with self-mode connectivity to achieve approximate unlearning comparable to SOTA methods on four datasets while staying in representation space.
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Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions
A survey organizing knowledge distillation techniques for addressing privacy, heterogeneity, communication, and personalization challenges in federated learning.