FedUP achieves fast, reversible one-shot federated unlearning via pluggable centroid-guided filters that reduce non-target knowledge loss.
Federated unlearning with knowledge distillation
11 Pith papers cite this work. Polarity classification is still indexing.
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DMBA maintains attack success rates above 80% for all backdoors in a distributed multi-target FL setting where baselines drop below 50%.
EASE closes three residual anchors in federated multimodal unlearning using bilateral displacement, cosine-sine decomposition, and forget lock, achieving near-retrain performance on forget and retain data.
Jellyfish enables zero-shot federated unlearning through synthetic proxy data generation, channel-restricted knowledge disentanglement, and a composite loss with repair to forget target data while retaining model utility.
A priority-aware learning-unlearning framework with orthogonal LoRA enables robust correction for device join/leave events in dynamic decentralized federated LLM fine-tuning.
Introduces Grouped Memorization Evaluation and FedMemPrune to remove unique memorized information in federated unlearning while preserving overlapping knowledge.
AFU-IC decouples client unlearning from global federated training in medical imaging and adds server-side invariance calibration to prevent relearning of erased data.
A complete pipeline for federated unlearning via knowledge distillation for efficient removal and a GAN-integrated classifier for visual evaluation of forgetting capacity.
FedQUIT performs on-device unlearning in federated learning by distilling from a virtual teacher that penalizes true-class confidence on forget data while preserving other output relationships, matching or exceeding prior methods with lower overhead than retraining.
IFF-FCU uses linear image feature fusion via Mixup to widen the forgetting boundary in federated client unlearning, yielding competitive error deviation from retrained models on medical imaging benchmarks.
A survey classifying machine unlearning into centralized (exact and approximate), distributed/irregular data, verification, and privacy/security categories with technique overviews.
citing papers explorer
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FedUP: One-Shot Federated Unlearning via Centroid-Guided Plug-in Filters
FedUP achieves fast, reversible one-shot federated unlearning via pluggable centroid-guided filters that reduce non-target knowledge loss.
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Act in Collusion: Distributed Multi-Target Backdoor Attacks in Federated Learning
DMBA maintains attack success rates above 80% for all backdoors in a distributed multi-target FL setting where baselines drop below 50%.
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EASE: Federated Multimodal Unlearning via Entanglement-Aware Anchor Closure
EASE closes three residual anchors in federated multimodal unlearning using bilateral displacement, cosine-sine decomposition, and forget lock, achieving near-retrain performance on forget and retain data.
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Jellyfish: Zero-Shot Federated Unlearning Scheme with Knowledge Disentanglement
Jellyfish enables zero-shot federated unlearning through synthetic proxy data generation, channel-restricted knowledge disentanglement, and a composite loss with repair to forget target data while retaining model utility.
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Priority-Aware Learning-Unlearning Correction for Dynamic Decentralized LoRA Fine-Tuning
A priority-aware learning-unlearning framework with orthogonal LoRA enables robust correction for device join/leave events in dynamic decentralized federated LLM fine-tuning.
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Rethinking Federated Unlearning via the Lens of Memorization
Introduces Grouped Memorization Evaluation and FedMemPrune to remove unique memorized information in federated unlearning while preserving overlapping knowledge.
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Asynchronous Federated Unlearning with Invariance Calibration for Medical Imaging
AFU-IC decouples client unlearning from global federated training in medical imaging and adds server-side invariance calibration to prevent relearning of erased data.
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Forgetting to Witness: Efficient Federated Unlearning and Its Visible Evaluation
A complete pipeline for federated unlearning via knowledge distillation for efficient removal and a GAN-integrated classifier for visual evaluation of forgetting capacity.
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FedQUIT: On-Device Federated Unlearning via a Quasi-Competent Virtual Teacher
FedQUIT performs on-device unlearning in federated learning by distilling from a virtual teacher that penalizes true-class confidence on forget data while preserving other output relationships, matching or exceeding prior methods with lower overhead than retraining.
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Image Feature Fusion-based Federated Client Unlearning (FCU)
IFF-FCU uses linear image feature fusion via Mixup to widen the forgetting boundary in federated client unlearning, yielding competitive error deviation from retrained models on medical imaging benchmarks.
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Machine Unlearning: A Comprehensive Survey
A survey classifying machine unlearning into centralized (exact and approximate), distributed/irregular data, verification, and privacy/security categories with technique overviews.