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Vertical Federated Unlearning on the Logistic Regression Model

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Forget-SVGD: Particle-Based Bayesian Federated Unlearning
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Higher-Order Truss Decomposition in Graphs
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User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization
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Fast Yet Effective Machine Unlearning
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Detection of non-coding RNAs on the basis of predicted secondary structure formation free energy change
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Communication-Efficient Learning of Deep Networks from Decentralized Data
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Descent-to-Delete: Gradient-Based Methods for Machine Unlearning
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Remember What You Want to Forget: Algorithms for Machine Unlearning
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Adaptive Machine Unlearning
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Athena: Probabilistic Verification of Machine Unlearning

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Machine Unlearning for Recommendation Systems: An Insight
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Fast and Accurate Class-Level Machine Unlearning using Impair–Repair and Noise-Induced Forgetting Mechanisms
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A Survey of Challenges and Opportunities in Vertical Federated Unlearning
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pMoEFU: Class-Level Federated Unlearning with Personalized Mixture of Experts
inferred← citeshttps://doi.org/10.1109/iciss63372.2025.11076308openalex-snapshot
An Analysis of Credit Card Fraud Detection using Machine Learning Algorithms
inferred← citeshttps://doi.org/10.1109/tkde.2025.3613659openalex-snapshot
FELEMN: Toward Efficient Feature-Level Machine Unlearning for Exact Privacy Protection
inferred← citeshttps://doi.org/10.1109/tnnls.2024.3478334openalex-snapshot
Federated Unlearning: A Survey on Methods, Design Guidelines, and Evaluation Metrics
inferred← citeshttps://doi.org/10.1109/tnse.2025.3637602openalex-snapshot
LVFUS: Vertical Federated Unlearning for Intelligent Network Security via Adaptive Optimizer Switching
inferred← citeshttps://doi.org/10.1109/tsc.2025.3536312openalex-snapshot
Vertical Federated Unlearning via Backdoor Certification
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A Survey on Federated Unlearning: Challenges, Methods, and Future Directions
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