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Certified Unlearning for Federated Recommendation
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-030-71782-7_35openalex-snapshot
Right to Be Forgotten in the Age of Machine Learning
inferredcites →https://doi.org/10.1007/978-3-031-70362-1_4openalex-snapshot
Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew Resilience
inferredcites →https://doi.org/10.1007/s11432-024-4123-4openalex-snapshot
Privacy-preserving explainable AI: a survey
inferredcites →https://doi.org/10.1016/j.ins.2024.121165openalex-snapshot
Higher-order knowledge-enhanced recommendation with heterogeneous hypergraph multi-attention
inferredcites →https://doi.org/10.1049/cit2.12362openalex-snapshot
A dual benchmarking study of facial forgery and facial forensics
inferredcites →https://doi.org/10.1098/rsta.2018.0083openalex-snapshot
Algorithms that remember: model inversion attacks and data protection law
inferredcites →https://doi.org/10.1109/icde53745.2022.00243openalex-snapshot
FedRecAttack: Model Poisoning Attack to Federated Recommendation
inferredcites →https://doi.org/10.1109/icdm.2018.00020openalex-snapshot
TADA: Trend Alignment with Dual-Attention Multi-task Recurrent Neural Networks for Sales Prediction
inferredcites →https://doi.org/10.1109/infocom48880.2022.9796721openalex-snapshot
The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining
inferredcites →https://doi.org/10.1109/iwqos52092.2021.9521274openalex-snapshot
FedEraser: Enabling Efficient Client-Level Data Removal from Federated Learning Models
inferredcites →https://doi.org/10.1109/mis.2020.3017205openalex-snapshot
FedRec: Federated Recommendation With Explicit Feedback
inferredcites →https://doi.org/10.1109/msp.2017.2765202openalex-snapshot
Generative Adversarial Networks: An Overview
inferredcites →https://doi.org/10.1109/sp.2015.35openalex-snapshot
Towards Making Systems Forget with Machine Unlearning
inferredcites →https://doi.org/10.1109/sp.2019.00031openalex-snapshot
Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks
inferredcites →https://doi.org/10.1109/tccn.2021.3084406openalex-snapshot
Fast-Convergent Federated Learning With Adaptive Weighting
inferredcites →https://doi.org/10.1109/tkde.2021.3101840openalex-snapshot
Network Alignment with Holistic Embeddings
inferredcites →https://doi.org/10.1109/tsp.2021.3106104openalex-snapshot
A Novel Framework for the Analysis and Design of Heterogeneous Federated Learning
inferredcites →https://doi.org/10.1109/wi-iat.2013.57openalex-snapshot
An Empirical Study of User Behaviors on Pinterest Social Network
inferredcites →https://doi.org/10.1145/2488388.2488393openalex-snapshot
Distributed large-scale natural graph factorization
inferredcites →https://doi.org/10.1145/3097983.3098202openalex-snapshot
On Sampling Strategies for Neural Network-based Collaborative Filtering
inferredcites →https://doi.org/10.1145/3219819.3220004openalex-snapshot
Neural Memory Streaming Recommender Networks with Adversarial Training
inferredcites →https://doi.org/10.1145/3289600.3290968openalex-snapshot
Federated Online Learning to Rank with Evolution Strategies
inferredcites →https://doi.org/10.1145/3289600.3290982openalex-snapshot
Social Attentional Memory Network
inferredcites →https://doi.org/10.1145/3308558.3313416openalex-snapshot
Adversarial Sampling and Training for Semi-Supervised Information Retrieval
inferredcites →https://doi.org/10.1145/3308560.3316457openalex-snapshot
Recommender Systems for Online Video Game Platforms: the Case of STEAM
inferredcites →https://doi.org/10.1145/3394486.3403176openalex-snapshot
FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems
inferredcites →https://doi.org/10.1145/3397271.3401165openalex-snapshot
GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection
inferredcites →https://doi.org/10.1145/3477495.3532685openalex-snapshot
Sequential/Session-based Recommendations
inferredcites →https://doi.org/10.1145/3488560.3498386openalex-snapshot
PipAttack: Poisoning Federated Recommender Systems for Manipulating Item Promotion
inferredcites →https://doi.org/10.1145/3534678.3539119openalex-snapshot
FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling
inferredcites →https://doi.org/10.1145/3539597.3570463openalex-snapshot
Federated Unlearning for On-Device Recommendation
inferredcites →https://doi.org/10.1145/3539618.3591722openalex-snapshot
Manipulating Federated Recommender Systems: Poisoning with Synthetic Users and Its Countermeasures
inferredcites →https://doi.org/10.1145/3543507.3583359openalex-snapshot
Interaction-level Membership Inference Attack Against Federated Recommender Systems
inferredcites →https://doi.org/10.1145/3547333openalex-snapshot
A Survey on the Fairness of Recommender Systems
inferredcites →https://doi.org/10.1145/3548455openalex-snapshot
A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions
inferredcites →https://doi.org/10.1145/3548456openalex-snapshot
A Generic Federated Recommendation Framework via Fake Marks and Secret Sharing
inferredcites →https://doi.org/10.1145/3560486openalex-snapshot
ReFRS: Resource-efficient Federated Recommender System for Dynamic and Diversified User Preferences
inferredcites →https://doi.org/10.1145/3564284openalex-snapshot
Bias and Debias in Recommender System: A Survey and Future Directions
inferredcites →https://doi.org/10.1145/3567420openalex-snapshot
Poisoning GNN-based Recommender Systems with Generative Surrogate-based Attacks
inferredcites →https://doi.org/10.1145/3581783.3612418openalex-snapshot
Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems
inferredcites →https://doi.org/10.1145/3594633openalex-snapshot
Quotation Recommendation for Multi-party Online Conversations Based on Semantic and Topic Fusion
inferredcites →https://doi.org/10.1145/3627158openalex-snapshot
Contrastive Self-supervised Learning in Recommender Systems: A Survey
inferredcites →https://doi.org/10.1145/3677328openalex-snapshot
Manipulating Recommender Systems: A Survey of Poisoning Attacks and Countermeasures
inferredcites →https://doi.org/10.1609/aaai.v35i5.16546openalex-snapshot
FedRec++: Lossless Federated Recommendation with Explicit Feedback
inferredcites →https://doi.org/10.48550/arxiv.1602.05629openalex-snapshot
Communication-Efficient Learning of Deep Networks from Decentralized Data
inferredcites →https://doi.org/10.48550/arxiv.1804.06872openalex-snapshot
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
inferredcites →https://doi.org/10.48550/arxiv.1906.04893openalex-snapshot
Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1907.05012openalex-snapshot
Making AI Forget You: Data Deletion in Machine Learning
inferredcites →https://doi.org/10.48550/arxiv.1909.00385openalex-snapshot
SDM: Sequential Deep Matching Model for Online Large-scale Recommender System
inferredcites →https://doi.org/10.48550/arxiv.2002.02126openalex-snapshot
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
inferredcites →https://doi.org/10.48550/arxiv.2002.10077openalex-snapshot
Approximate Data Deletion from Machine Learning Models
inferredcites →https://doi.org/10.48550/arxiv.2009.03376openalex-snapshot
Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering
inferredcites →https://doi.org/10.48550/arxiv.2101.04898openalex-snapshot
Unlearnable Examples: Making Personal Data Unexploitable
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-032-21168-2_3openalex-snapshot
ClusLazyFR: An Efficient Federated Recommendation Scheme via User Clustering and Lazy Aggregation
inferred← citeshttps://doi.org/10.1007/978-981-92-0263-8_22openalex-snapshot
LUMOS: LLM-Driven Contrastive Federated Sequential Recommendation
inferred← citeshttps://doi.org/10.1007/978-981-95-9846-5_18openalex-snapshot
Noise-Resilient Small Object Detection with Octave and Cross-Frequency Convolution
inferred← citeshttps://doi.org/10.1007/s11227-025-07478-2openalex-snapshot
FedSSU: flexible and efficient decentralized unlearning for federated learning
inferred← citeshttps://doi.org/10.1016/j.comcom.2026.108497openalex-snapshot
Enhancing federated unlearning using catastrophic forgetting in heterogeneous Industrial Internet of Things
inferred← citeshttps://doi.org/10.1016/j.eswa.2026.132558openalex-snapshot
SoK: Understanding backdoor attacks & defenses in federated learning
inferred← citeshttps://doi.org/10.1016/j.inffus.2026.104712openalex-snapshot
Federated recommender systems as distributed knowledge fusion: Techniques, challenges, and future directions
inferred← citeshttps://doi.org/10.1016/j.neunet.2025.107667openalex-snapshot
Enhancing partition distinction: A contrastive policy to recommendation unlearning
inferred← citeshttps://doi.org/10.1109/2575-8411.2026.00143openalex-snapshot
Scale: Sensitivity-Aware Federated Unlearning with Information Freshness Optimization for Mobile Edge Computing
inferred← citeshttps://doi.org/10.1109/icme59968.2025.11209581openalex-snapshot
Global Perception Federated Recommender System for Click-Through Rate Prediction
inferred← citeshttps://doi.org/10.1109/tkde.2025.3606643openalex-snapshot
Personalized Recommendation Models in Federated Settings: A Survey
inferred← citeshttps://doi.org/10.1109/tsc.2026.3685363openalex-snapshot
AdaFUN: Enhancing Federated Unlearning With Adaptive Local Step in Edge Computing
inferred← citeshttps://doi.org/10.1145/3749990openalex-snapshot
Cross-User Federated Recommendation Unlearning
inferred← citeshttps://doi.org/10.1145/3769811openalex-snapshot
P 2 FedRec: Towards Privacy-Preserving and Personalized Federated Recommendation via Relationship Awareness
inferred← citeshttps://doi.org/10.1145/3793855openalex-snapshot
Negative Sampling in Recommendation: A Survey and Future Directions
inferred← citeshttps://doi.org/10.1145/3803545openalex-snapshot
Privacy Matters: Data Attack to Make User Preferences Unlearnable in Recommendation
inferred← citeshttps://doi.org/10.1145/3807451openalex-snapshot
Towards Reliable Forgetting: A Survey on Machine Unlearning Verification
inferred← citeshttps://doi.org/10.2139/ssrn.5963078openalex-snapshot
Selective Forgetting in Hierarchical Federated Learning: Towards Global Model Unlearning of Client Data
inferred← citeshttps://doi.org/10.3390/e27111112openalex-snapshot
A Federated Recommendation System with a Dual-Layer Multi-Head Attention Network and Regularization Strategy
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