Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead | Paper Citation Record

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This paper cites write newline.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead write newline

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This paper cites Investigating Under and Overfitting in Wasserstein Generative Adversarial Networks.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Investigating Under and Overfitting in Wasserstein Generative Adversarial Networks

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This paper cites FedSyn: Synthetic Data Generation using Federated Learning.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead FedSyn: Synthetic Data Generation using Federated Learning

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Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Unresolved cited work

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This paper cites Analysis of representations for domain adaptation.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Analysis of representations for domain adaptation

Reference 5

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This paper cites Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer

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This paper cites Infogan: Interpretable representation learning by information maximizing generative adversarial nets.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Infogan: Interpretable representation learning by information maximizing generative adversarial nets

Reference 7

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This paper cites J., Manoel, A., Joshi, G., Sim, R., and Dimitriadis, D.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead J., Manoel, A., Joshi, G., Sim, R., and Dimitriadis, D

Reference 8

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This paper cites Stargan: Unified generative adversarial networks for multi-domain image-to-image translation.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Stargan: Unified generative adversarial networks for multi-domain image-to-image translation

Reference 9

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This paper cites A downsampled variant of ImageNet as an alternative to the CIFAR datasets.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead A downsampled variant of ImageNet as an alternative to the CIFAR datasets

Reference 10

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This paper cites Learning from multiple sources.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Learning from multiple sources

Reference 11

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This paper cites Tackling data heterogeneity in federated learning with class prototypes.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Tackling data heterogeneity in federated learning with class prototypes

Reference 12

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This paper cites A hierarchical knowledge transfer framework for heterogeneous federated learning.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead A hierarchical knowledge transfer framework for heterogeneous federated learning

Reference 13

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This paper cites Q., Li, A., and Kung, H.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Q., Li, A., and Kung, H

Reference 14

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This paper cites Calibrating noise to sensitivity in private data analysis.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Calibrating noise to sensitivity in private data analysis

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This paper cites The algorithmic foundations of differential privacy.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead The algorithmic foundations of differential privacy

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Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead and Liu, P

Reference 17

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This paper cites Ensemble attention distillation for privacy-preserving federated learning.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Ensemble attention distillation for privacy-preserving federated learning

Reference 18

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This paper cites Generative adversarial nets.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Generative adversarial nets

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Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Unresolved cited work

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Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Unresolved cited work

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This paper cites Md-gan: Multi-discriminator generative adversarial networks for distributed datasets.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Md-gan: Multi-discriminator generative adversarial networks for distributed datasets

Reference 22

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This paper cites Deep residual learning for image recognition.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Deep residual learning for image recognition

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This paper cites Distilling the Knowledge in a Neural Network.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Distilling the Knowledge in a Neural Network

Reference 24

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Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Neural networks for machine learning

Reference 25

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This paper cites Understanding convergence and generalization in federated learning through feature learning theory.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Understanding convergence and generalization in federated learning through feature learning theory

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This paper cites https://www.kaggle.com/datasets/ambityga/imagenet100.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead https://www.kaggle.com/datasets/ambityga/imagenet100

Reference 27

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This paper cites and Szegedy, C.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead and Szegedy, C

Reference 28

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This paper cites B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A

Reference 29

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Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead P., Kale, S., Mohri, M., Reddi, S., Stich, S., and Suresh, A

Reference 30

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This paper cites A style-based generator architecture for generative adversarial networks.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead A style-based generator architecture for generative adversarial networks

Reference 31

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Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Detecting change in data streams

Reference 32

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Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Fed FN: F eature normalization for alleviating data heterogeneity problem in federated learning

Reference 33

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Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Adam: A Method for Stochastic Optimization

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

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Learning multiple layers of features from tiny images

Reference 35

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Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Unresolved cited work

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This paper cites Fedtp: Federated learning by transformer personalization.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Fedtp: Federated learning by transformer personalization

Reference 37

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This paper cites K., Zaheer, M., Sanjabi, M., Talwalkar, A., and Smith, V.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead K., Zaheer, M., Sanjabi, M., Talwalkar, A., and Smith, V

Reference 38

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This paper cites Ifl-gan: Improved federated learning generative adversarial network with maximum mean discrepancy model aggregation.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Ifl-gan: Improved federated learning generative adversarial network with maximum mean discrepancy model aggregation

Reference 39

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This paper cites On the convergence of fed A vg on Non-IID data.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead On the convergence of fed A vg on Non-IID data

Reference 40

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Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead and Zhan, D.-C

Reference 41

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Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Variance Reduced Local SGD with Lower Communication Complexity

Reference 42

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Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead U., and Jaggi, M

Reference 43

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Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead and Hutter, F

Reference 44

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This paper cites Personalized federated learning through local memorization.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Personalized federated learning through local memorization

Reference 45

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Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Unresolved cited work

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Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Local learning matters: Rethinking data heterogeneity in federated learning

Reference 47

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This paper cites Overcoming client data deficiency in federated learning by exploiting unlabeled data on the server.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Overcoming client data deficiency in federated learning by exploiting unlabeled data on the server

Reference 48

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This paper cites Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Reference 49

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This paper cites FedGAN: Federated Generative Adversarial Networks for Distributed Data.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead FedGAN: Federated Generative Adversarial Networks for Distributed Data

Reference 50

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Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Communication-Efficient Federated Distillation

Reference 51

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This paper cites Stylegan-xl: Scaling stylegan to large diverse datasets.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Stylegan-xl: Scaling stylegan to large diverse datasets

Reference 52

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This paper cites and Ben-David, S.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead and Ben-David, S

Reference 53

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This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 54

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This paper cites M., Kim, M.-H., Chung, T.-M., Huang, C., and Liu, X.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead M., Kim, M.-H., Chung, T.-M., Huang, C., and Liu, X

Reference 55

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Observation d968cbe1-f0c6-4d3f-9a20-9837801fff7d · outbound

This paper cites Virtual homogeneity learning: D efending against data heterogeneity in federated learning.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Virtual homogeneity learning: D efending against data heterogeneity in federated learning

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:50:59.100899Z

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Observation d2958be6-8d62-4a63-b540-bb520cde0998 · outbound

This paper cites Fed Impro : M easuring and improving client update in federated learning.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Fed Impro : M easuring and improving client update in federated learning

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:50:59.092871Z

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This paper cites Federated learning with matched averaging.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Federated learning with matched averaging

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:50:59.084797Z

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Observation 3c8d5caa-62ca-472b-9f2a-bd70f4543908 · outbound

This paper cites Dafkd: Domain-aware federated knowledge distillation.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Dafkd: Domain-aware federated knowledge distillation

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:50:59.076968Z

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Observation e7dc6797-adfb-4197-9e4b-135f42691c56 · outbound

This paper cites Fed CDA: F ederated learning with cross-rounds divergence-aware aggregation.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Fed CDA: F ederated learning with cross-rounds divergence-aware aggregation

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:50:59.069022Z

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Observation 94426064-1c95-4a36-8853-a458346b8ee6 · outbound

This paper cites an unresolved cited work.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-08-08T15:50:59.061076Z

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Observation 6a48cb36-9104-448e-8807-81f1c720ad5b · outbound

This paper cites Fed Med-GAN : Fed erated domain translation on unsupervised cross-modality brain image synthesis.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Fed Med-GAN : Fed erated domain translation on unsupervised cross-modality brain image synthesis

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:50:59.053249Z

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Observation f93ab761-a519-43a3-8a99-f124d1d45a9b · outbound

This paper cites Do generated data always help contrastive learning? In The Twelfth International Conference on Learning Representations, 2024.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Do generated data always help contrastive learning? In The Twelfth International Conference on Learning Representations, 2024

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:50:59.045223Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3a7ea86b-89a0-40c0-890b-17d5a54818f6 · outbound

This paper cites An efficient federated distillation learning system for multitask time series classification.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead An efficient federated distillation learning system for multitask time series classification

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:50:59.036266Z

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Observation aaaf8ede-9e4d-4a68-bbce-73499c128bb3 · outbound

This paper cites Federated generative model on multi-source heterogeneous data in iot.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Federated generative model on multi-source heterogeneous data in iot

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:50:59.027755Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 71418943-372b-498e-ab97-e497f6101b75 · outbound

This paper cites Improving gans with a dynamic discriminator.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Improving gans with a dynamic discriminator

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:50:59.019493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c9df243f-831e-45ec-ba48-445d6d72b9cb · outbound

This paper cites Fed Fed: F eature distillation against data heterogeneity in federated learning.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Fed Fed: F eature distillation against data heterogeneity in federated learning

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:50:59.011000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 317e9220-5d4c-41a4-96a6-ebbd84d643d5 · outbound

This paper cites Local-Global Knowledge Distillation in Heterogeneous Federated Learning with Non-IID Data.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Local-Global Knowledge Distillation in Heterogeneous Federated Learning with Non-IID Data

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-08T15:50:58.814948Z

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Observation 3929fcad-6b9e-4aab-97b4-2767f1e65322 · outbound

This paper cites Fed Disco : Federated learning with discrepancy-aware collaboration.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Fed Disco : Federated learning with discrepancy-aware collaboration

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:50:59.002695Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a2e76c80-4c74-40d3-88b6-bd5d0a31d0bd · outbound

This paper cites Model Collapse in the Self-Consuming Chain of Diffusion Finetuning: A Novel Perspective from Quantitative Trait Modeling.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Model Collapse in the Self-Consuming Chain of Diffusion Finetuning: A Novel Perspective from Quantitative Trait Modeling

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-08T15:50:58.821069Z

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Observation e5710e9d-edac-4573-a33b-97996fd62dbb · outbound

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Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Unresolved cited work

Reference 71

Resolution: unresolved; raw_fallback, observed 2026-08-08T15:50:58.993983Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0989c028-1eb2-4c92-836a-1eb6aca71257 · outbound

This paper cites Y., and Zomaya, A.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Y., and Zomaya, A

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:50:58.985148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1305f40b-7b9d-4608-a602-c0a0c974c796 · outbound

This paper cites Fedtgp: Trainable global prototypes with adaptive-margin-enhanced contrastive learning for data and model heterogeneity in federated learning.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Fedtgp: Trainable global prototypes with adaptive-margin-enhanced contrastive learning for data and model heterogeneity in federated learning

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:50:58.976688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation de7dee1b-3a5c-42b9-ab3e-1d6ce68cf852 · outbound

This paper cites Fedzkt: Zero-shot knowledge transfer towards resource-constrained federated learning with heterogeneous on-device models.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Fedzkt: Zero-shot knowledge transfer towards resource-constrained federated learning with heterogeneous on-device models

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:50:58.967852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 46a00e94-132e-46b0-9276-9f5a3cf2f4f1 · outbound

This paper cites an unresolved cited work.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Unresolved cited work

Reference 75

Resolution: unresolved; raw_fallback, observed 2026-08-08T15:50:58.958309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 23f0684c-ba60-4895-9808-a233b46fb448 · outbound

This paper cites an unresolved cited work.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Unresolved cited work

Reference 76

Resolution: unresolved; raw_fallback, observed 2026-08-08T15:50:58.949356Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Reference resolution

Outbound references

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