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Federated User Modeling from Hierarchical Information

https://doi.org/10.1145/3560485
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What this work says

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

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Coefficient of Determination
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Test Equating: Methods and Practices
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Federated Recommendation Systems
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Federated Multi-view Matrix Factorization for Personalized Recommendations
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DP-FL: a novel differentially private federated learning framework for the unbalanced data
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ASFGNN: Automated separated-federated graph neural network
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Emerging trends in federated learning: from model fusion to federated X learning
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Federated learning of predictive models from federated Electronic Health Records
inferredcites →https://doi.org/10.1023/a:1011175525451openalex-snapshot
Predictive Statistical Models for User Modeling
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Federated Learning with Bayesian Differential Privacy
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Personalized Travel Package Recommendation
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2020 International Joint Conference on Neural Networks (IJCNN)
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Optimizing Federated Learning on Non-IID Data with Reinforcement Learning
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Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems
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FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare
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SecureBoost: A Lossless Federated Learning Framework
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Federated Forest
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Federated Learning With Differential Privacy: Algorithms and Performance Analysis
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Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
inferredcites →https://doi.org/10.1109/tit.2022.3192506openalex-snapshot
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EKT: Exercise-Aware Knowledge Tracing for Student Performance Prediction
inferredcites →https://doi.org/10.1109/tkde.2019.2941938openalex-snapshot
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inferredcites →https://doi.org/10.1109/tnnls.2019.2944481openalex-snapshot
Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data
inferredcites →https://doi.org/10.1109/tnnls.2019.2953131openalex-snapshot
Communication-Efficient Federated Deep Learning With Layerwise Asynchronous Model Update and Temporally Weighted Aggregation
inferredcites →https://doi.org/10.1109/tsmc.2017.2665038openalex-snapshot
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inferredcites →https://doi.org/10.1109/tsp.2006.881199openalex-snapshot
$rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
inferredcites →https://doi.org/10.1109/visual.1994.346316openalex-snapshot
User modeling for adaptive visualization systems
inferredcites →https://doi.org/10.1145/1099554.1099747openalex-snapshot
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inferredcites →https://doi.org/10.1145/1390156.1390294openalex-snapshot
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inferredcites →https://doi.org/10.1145/1462198.1462203openalex-snapshot
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inferredcites →https://doi.org/10.1145/2660267.2660348openalex-snapshot
RAPPOR
inferredcites →https://doi.org/10.1145/2699670openalex-snapshot
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inferredcites →https://doi.org/10.1145/2736277.2741667openalex-snapshot
A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems
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MCNE
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inferredcites →https://doi.org/10.1145/3308558.3313751openalex-snapshot
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Efficient Neural Matrix Factorization without Sampling for Recommendation
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Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
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inferredcites →https://doi.org/10.1145/3418283openalex-snapshot
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inferredcites →https://doi.org/10.1186/s40537-019-0197-0openalex-snapshot
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inferredcites →https://doi.org/10.1371/journal.pone.0230706openalex-snapshot
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inferredcites →https://doi.org/10.1609/aaai.v31i1.10740openalex-snapshot
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inferredcites →https://doi.org/10.1609/aaai.v34i04.6096openalex-snapshot
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inferredcites →https://doi.org/10.1631/fitee.1700813openalex-snapshot
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inferredcites →https://doi.org/10.1631/fitee.2200268openalex-snapshot
Federated unsupervised representation learning
inferredcites →https://doi.org/10.21105/joss.00432openalex-snapshot
Augmentor: An Image Augmentation Library for Machine Learning
inferredcites →https://doi.org/10.2200/s00960ed2v01y201910aim043openalex-snapshot
Federated Learning
inferredcites →https://doi.org/10.24963/ijcai.2021/217openalex-snapshot
LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy
inferredcites →https://doi.org/10.4249/scholarpedia.2088openalex-snapshot
Signal-to-noise ratio
inferredcites →https://doi.org/10.48550/arxiv.1710.06963openalex-snapshot
Learning Differentially Private Recurrent Language Models
inferredcites →https://doi.org/10.48550/arxiv.1712.07557openalex-snapshot
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inferredcites →https://doi.org/10.48550/arxiv.1902.00146openalex-snapshot
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inferredcites →https://doi.org/10.48550/arxiv.1902.11175openalex-snapshot
One-Shot Federated Learning
inferredcites →https://doi.org/10.48550/arxiv.1907.02189openalex-snapshot
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inferredcites →https://doi.org/10.48550/arxiv.1909.12946openalex-snapshot
Towards Federated Graph Learning for Collaborative Financial Crimes Detection
inferredcites →https://doi.org/10.48550/arxiv.1910.03581openalex-snapshot
FedMD: Heterogenous Federated Learning via Model Distillation
inferredcites →https://doi.org/10.48550/arxiv.1912.04042openalex-snapshot
Element Level Differential Privacy: The Right Granularity of Privacy
inferredcites →https://doi.org/10.48550/arxiv.1912.06733openalex-snapshot
Private Federated Learning with Domain Adaptation
inferredcites →https://doi.org/10.48550/arxiv.1912.12795openalex-snapshot
Quantifying the Performance of Federated Transfer Learning
inferredcites →https://doi.org/10.48550/arxiv.1912.13075openalex-snapshot
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inferredcites →https://doi.org/10.48550/arxiv.2002.05516openalex-snapshot
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inferredcites →https://doi.org/10.48550/arxiv.2002.10619openalex-snapshot
Three Approaches for Personalization with Applications to Federated Learning
inferredcites →https://doi.org/10.48550/arxiv.2007.04618openalex-snapshot
Federated Learning of User Authentication Models
inferredcites →https://doi.org/10.48550/arxiv.2102.04925openalex-snapshot
FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation
inferredcites →https://doi.org/10.48550/arxiv.2106.13423openalex-snapshot
Federated Graph Classification over Non-IID Graphs
inferredcites →https://doi.org/10.5555/1756006.1953039openalex-snapshot
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion

What points at this work

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

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