Hugging Face Trending Papers
Efficient Exact Unlearning for Federated Zeroth-Order Optimization
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arXiv AI
Oct 27
Efficient Exact Unlearning for Federated Zeroth-Order Optimization
arXiv:2610.35211v1 Announce Type: cross Abstract: Federated zeroth-order optimization (FedZOO) reduces the memory and communication costs of collaborative training for edge intelligence by avoiding b...
arXiv AI
Jun 9
Causal Unlearning in Collaborative Optimization: Exact and Approximate Influence Reversal under Adversarial Contributions
arXiv:2605. 20341v2 Announce Type: replace-cross Abstract: Federated learning systems must support data deletion requests to comply with privacy regulations, yet retraining from scratch after each deletion is computationally prohibitive.
arXiv AI
Oct 1
Unmerge: Efficient Machine Unlearning via Task Arithmetic
The paper introduces Unmerge, an efficient machine unlearning algorithm that treats unlearning as the inverse of task arithmetic. By representing the forget component as a low‑rank basis at each layer, Unmerge optimizes three goals—matching the merged vector, suppressing leakage, and bounding correction size—to limit forget leakage and retain damage. Experiments on ResNet‑50, ViT‑S/16, and Llama‑3.2‑3B show significant performance gains over existing methods while maintaining privacy and feature‑distribution fidelity.
arXiv Machine Learning
Oct 27
Not All Retained Clients Are Equal: Client Selection for Federated Unlearning
arXiv:2610.31786v1 Announce Type: new Abstract: Federated unlearning (FU) aims to remove a designated target client's influence from a trained model without the prohibitive cost of retraining from sc...
arXiv Machine Learning
Jun 16
Exact Federated Continual Unlearning for Ridge Heads on Frozen Foundation Models
arXiv:2603. 12977v3 Announce Type: replace Abstract: Foundation models are commonly deployed as frozen feature extractors with a small trainable head to adapt to private, user-generated data in federated settings.
Hugging Face Trending Papers
Sep 30
Unmerge: Efficient Machine Unlearning via Task Arithmetic
Approximate machine unlearning seeks to remove the influence of a forget set from a trained model without full retraining. Existing gradient-based methods require data-dependent hyperparameter search,...