Fed-AugMix: Balancing Privacy and Utility via Data Augmentation | Paper Citation Record

Observation 5144f640-e5df-445b-9fdd-83b405192f25 · outbound

This paper cites Communication-Efficient Learning of Deep Networks from Decentralized Data.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Communication-Efficient Learning of Deep Networks from Decentralized Data

Reference 1

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Observation 30bd8d4e-9a2c-4406-b9fc-a2fe2ea26bee · outbound

This paper cites Communication-efficient learning of deep networks from decentralized data,.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Communication-efficient learning of deep networks from decentralized data,

Reference 2

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Observation 153e0476-4249-47f6-a8a7-ca629410e542 · outbound

This paper cites Federated Optimization: Distributed Machine Learning for On-Device Intelligence.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Federated Optimization: Distributed Machine Learning for On-Device Intelligence

Reference 3

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Observation a2074923-b222-478c-a6bb-1341eb414fef · outbound

This paper cites Federated Learning: Strategies for Improving Communication Efficiency.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Federated Learning: Strategies for Improving Communication Efficiency

Reference 4

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Observation 62b73346-fe58-43d9-b740-0121e21edbd5 · outbound

This paper cites Deep Leakage from Gradients.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Deep Leakage from Gradients

Reference 5

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This paper cites Inverting Gradients -- How easy is it to break privacy in federated learning?.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Inverting Gradients -- How easy is it to break privacy in federated learning?

Reference 6

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Observation 677ddfc3-fda7-4c12-b958-e11272e7dcda · outbound

This paper cites iDLG: Improved Deep Leakage from Gradients.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation iDLG: Improved Deep Leakage from Gradients

Reference 7

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Observation 8afc7cbb-6e81-4c3d-9d80-cbcd89677486 · outbound

This paper cites See through Gradients: Image Batch Recovery via GradInversion.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation See through Gradients: Image Batch Recovery via GradInversion

Reference 8

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Observation e5eaa536-2cd4-4a49-b81b-5c9b0411a0b9 · outbound

This paper cites Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption

Reference 9

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Observation 5d3c6765-3d48-4145-ba85-c43304af0dfa · outbound

This paper cites How to share a secret,.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation How to share a secret,

Reference 10

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Observation 545ac669-c464-4ee5-85cf-4087e17e3bcc · outbound

This paper cites Safeguarding cryptographic keys,.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Safeguarding cryptographic keys,

Reference 11

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This paper cites Practical secure aggregation for privacy-preserving ma- chine learning,.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Practical secure aggregation for privacy-preserving ma- chine learning,

Reference 12

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Observation 868e0bc2-84ff-4bce-af96-964369fb3c2f · outbound

This paper cites Deep learning with differential privacy,.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Deep learning with differential privacy,

Reference 13

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Observation 4750cd40-56a9-45b6-8e2e-14e53f964659 · outbound

This paper cites Deep gradient compression: Reducing the communication band- width for distributed training,.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Deep gradient compression: Reducing the communication band- width for distributed training,

Reference 14

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Observation ea1be1b1-83e9-4e18-a8bf-a62845f8e7f2 · outbound

This paper cites Gradient disaggregation: Breaking privacy in federated learning by reconstructing the user participant matrix,.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Gradient disaggregation: Breaking privacy in federated learning by reconstructing the user participant matrix,

Reference 15

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Observation 702bd2fe-cc63-453d-8a24-1e6d026bf0f7 · outbound

This paper cites Gradient- leakage resilient federated learning,.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Gradient- leakage resilient federated learning,

Reference 16

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Observation 15705edb-9d1a-403a-9e98-faeae1fc9e49 · outbound

This paper cites Differentially private federated learning on heterogeneous data,.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Differentially private federated learning on heterogeneous data,

Reference 17

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Observation 4f4cec85-0c87-4227-84c7-ff0781f6b5f0 · outbound

This paper cites A fine-grained differentially private federated learning against leakage from gradients,.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation A fine-grained differentially private federated learning against leakage from gradients,

Reference 18

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Observation 253742e9-329a-428e-bf9d-a2c919358152 · outbound

This paper cites Performance-enhanced federated learning with differential privacy for internet of things,.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Performance-enhanced federated learning with differential privacy for internet of things,

Reference 19

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Observation 05d15179-062d-4dc5-a6d5-4b7487145c54 · outbound

This paper cites Deep Residual Learning for Image Recognition.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Deep Residual Learning for Image Recognition

Reference 20

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Observation 3f328b1d-1546-4239-ae7f-bd17c202427e · outbound

This paper cites mixup: Beyond empirical risk minimization,.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation mixup: Beyond empirical risk minimization,

Reference 21

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Observation 6d19be51-7b96-499b-a471-af08658e2680 · outbound

This paper cites Between-class learning for image classification,.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Between-class learning for image classification,

Reference 22

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This paper cites Mixup as locally linear out-of-manifold regularization,.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Mixup as locally linear out-of-manifold regularization,

Reference 23

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Observation fc0588a6-3e12-4768-a4ca-b675ab5da2c0 · outbound

This paper cites Autoaugment: Learning augmentation policies from data,.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Autoaugment: Learning augmentation policies from data,

Reference 24

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Observation 0e621f61-5572-450b-b61f-842b26d1856d · outbound

This paper cites AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

Reference 25

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Observation 552dc4f7-515c-4d6c-8a0b-f580548cd1b5 · outbound

This paper cites Mitigating Data Heterogeneity in Federated Learning with Data Augmentation.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Mitigating Data Heterogeneity in Federated Learning with Data Augmentation

Reference 26

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This paper cites Data- augmentation-based federated learning,.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Data- augmentation-based federated learning,

Reference 27

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Observation 01d40d91-ed0a-413d-9c06-17c70fc3ecdb · outbound

This paper cites Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning,.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning,

Reference 28

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Observation ab785b6e-4ff5-4487-9dad-29603fcd25d9 · outbound

This paper cites Exploiting unintended feature leakage in collaborative learning,.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Exploiting unintended feature leakage in collaborative learning,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:49:38.491274Z

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Observation 1a82f7a5-d738-4482-a50e-cdf136b4da0a · outbound

This paper cites Model inversion attacks that exploit confidence information and basic countermeasures,.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Model inversion attacks that exploit confidence information and basic countermeasures,

Reference 30

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This paper cites Information theory and privacy in data banks,.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Information theory and privacy in data banks,

Reference 31

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This paper cites A source coding problem for sources with additional outputs to keep secret from the receiver or wiretappers (corresp.),.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation A source coding problem for sources with additional outputs to keep secret from the receiver or wiretappers (corresp.),

Reference 32

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This paper cites Utility- privacy tradeoffs in databases: An information-theoretic approach,.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Utility- privacy tradeoffs in databases: An information-theoretic approach,

Reference 33

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This paper cites On the relation between identifiability, differential privacy, and mutual- information privacy,.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation On the relation between identifiability, differential privacy, and mutual- information privacy,

Reference 34

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Observation 9e2bcc57-09a6-455c-b022-86f1b6b6aa9f · outbound

This paper cites An estimation-theoretic view of privacy,.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation An estimation-theoretic view of privacy,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:49:38.401596Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation af955242-ad66-4798-8813-508603951c07 · outbound

This paper cites Trading Off Privacy, Utility and Efficiency in Federated Learning.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Trading Off Privacy, Utility and Efficiency in Federated Learning

Reference 36

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Observation 2586436d-54f4-471f-a73b-94efb03043ae · outbound

This paper cites Learning privacy preserving encodings through adversarial training,.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Learning privacy preserving encodings through adversarial training,

Reference 37

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Observation edce0f01-dff4-4277-8d4a-0c01febfb2a3 · outbound

This paper cites Fl- 11 apb: Balancing privacy protection and performance optimization for adversarial training in federated learning,.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Fl- 11 apb: Balancing privacy protection and performance optimization for adversarial training in federated learning,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:49:38.375378Z

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Observation 08826c0d-6f3a-4a4c-b4d5-a0a62e59a5d9 · outbound

This paper cites Theoretically Principled Federated Learning for Balancing Privacy and Utility.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Theoretically Principled Federated Learning for Balancing Privacy and Utility

Reference 39

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Observation fa7fa78c-64b1-4828-9d80-4056da3b516f · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation mixup: Beyond Empirical Risk Minimization

Reference 40

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Observation e7345a60-acf2-403c-81f8-13b08c26a51c · outbound

This paper cites Data augmentation: A comprehensive survey of modern approaches,.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Data augmentation: A comprehensive survey of modern approaches,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:49:38.360054Z

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This paper cites Learning with pseudo-ensembles,.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Learning with pseudo-ensembles,

Reference 42

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Observation 5a77e4b6-477f-4ee3-8958-b3bc0430f568 · outbound

This paper cites Improv- ing the robustness of deep neural networks via stability training,.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Improv- ing the robustness of deep neural networks via stability training,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:49:38.310160Z

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This paper cites Adversarial logit pairing,.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Adversarial logit pairing,

Reference 44

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Observation 64e65f9a-23f4-4bf9-998a-05815c40c1d8 · outbound

This paper cites Mnist hand- written digit database,.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Mnist hand- written digit database,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:49:38.275981Z

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This paper cites Learning multiple layers of features from tiny images,.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Learning multiple layers of features from tiny images,

Reference 46

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This paper cites Gradient- based learning applied to document recognition,.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Gradient- based learning applied to document recognition,

Reference 47

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This paper cites Available: https://api.semanticscholar.org/ CorpusID:207229839.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Available: https://api.semanticscholar.org/ CorpusID:207229839

Reference 2015

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Observation 79aa42e4-bbdf-4b48-984d-d3e744d82ef6 · outbound

This paper cites Available: https://api.semanticscholar.org/ CorpusID:210064455.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Available: https://api.semanticscholar.org/ CorpusID:210064455

Reference 2020

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Observation 20f577e7-2d9c-4413-a2d1-f92cb0552645 · outbound

This paper cites Available: http://papers.nips.cc/paper/ 5487-learning-with-pseudo-ensembles.pdf.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Available: http://papers.nips.cc/paper/ 5487-learning-with-pseudo-ensembles.pdf

Reference 3373

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:49:38.326751Z

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