Paper Citation Record · LEDGER

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing

As of 10 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 1 inbound Pith citation observation for arXiv:2502.03092.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.03092 v1

Coverage vector

measured 63 of 63 reference resolution

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T19:21:36.579610Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-05T19:21:36.636161Z

Reference resolution

63 of 63 outbound references displayed

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External citation measurements

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Outbound references

Observation 15c6555a-ec33-4fa6-8f31-a39f3f18df60 · outbound

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

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Communication-efficient learning of deep networks from decentralized data,

Reference 1

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Observation e1906a9d-2f2a-4798-9696-ad3194f4ee91 · outbound

This paper cites A review of applications in federated learning,.

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing A review of applications in federated learning,

Reference 2

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Observation 64c0f0ed-89df-4ea9-85d6-6706538213f1 · outbound

This paper cites Large scale distributed deep networks,.

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Large scale distributed deep networks,

Reference 3

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This paper cites Demystifying parallel and distributed deep learning: An in-depth concurrency analysis,.

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Demystifying parallel and distributed deep learning: An in-depth concurrency analysis,

Reference 4

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Observation ab0bd9f0-17c5-46c3-a825-7b41d3bcf922 · outbound

This paper cites Prior: Personalized prior for reactivating the information overlooked in federated learning,.

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Prior: Personalized prior for reactivating the information overlooked in federated learning,

Reference 5

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Observation 77729d10-a5f5-42fa-9f42-76227660ebdc · outbound

This paper cites Defta: A plug-and-play peer-to-peer decentralized federated learning framework,.

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Defta: A plug-and-play peer-to-peer decentralized federated learning framework,

Reference 6

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Observation 791481c9-7c59-49f3-9fa7-9e62ac16872b · outbound

This paper cites Robust and communication-efficient federated learning from non-iid data,.

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Robust and communication-efficient federated learning from non-iid data,

Reference 7

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This paper cites Communication-efficient federated learning with compensated overlap-fedavg,.

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Communication-efficient federated learning with compensated overlap-fedavg,

Reference 8

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This paper cites Advances and open problems in federated learning,.

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Advances and open problems in federated learning,

Reference 9

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Observation 49f9d938-282f-488e-a577-b7e11b2ec9a3 · outbound

This paper cites Language mod- els are few-shot learners,.

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Language mod- els are few-shot learners,

Reference 10

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Observation 7bafffc4-7293-4da1-b1bb-c48d4d7eab8d · outbound

This paper cites An empirical study of parameter efficient fine-tuning on vision-language pre-train model,.

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing An empirical study of parameter efficient fine-tuning on vision-language pre-train model,

Reference 11

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Observation 545f38d1-6a40-4aaa-8ede-84c913a31467 · outbound

This paper cites UNITE: multitask learning with sufficient feature for dense prediction,.

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing UNITE: multitask learning with sufficient feature for dense prediction,

Reference 12

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Observation 80afe1fe-ada7-4b0e-8c0e-ce6cd979a284 · outbound

This paper cites Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,.

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,

Reference 13

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This paper cites Towards federated learning at scale: System design,.

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Towards federated learning at scale: System design,

Reference 14

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Observation ca46bb68-85b1-4bf5-95c0-4a544a3762e7 · outbound

This paper cites Scalable distributed dnn training using commodity gpu cloud computing,.

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Scalable distributed dnn training using commodity gpu cloud computing,

Reference 15

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Observation 6ddfb64d-eaf0-4243-93af-79fef21a54a4 · outbound

This paper cites Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training.

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training

Reference 16

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This paper cites Gradient sparsification for communication-efficient distributed optimization,.

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Gradient sparsification for communication-efficient distributed optimization,

Reference 17

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Observation dc619a45-c6ed-4009-8f6f-02994a924cf0 · outbound

This paper cites Qsgd: Communication-efficient sgd via gradient quantization and encoding,.

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Qsgd: Communication-efficient sgd via gradient quantization and encoding,

Reference 18

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E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing signsgd: Compressed optimisation for non-convex problems,

Reference 19

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E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Error feedback fixes signsgd and other gradient compression schemes,

Reference 20

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E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Knowledge distillation: A survey,

Reference 21

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E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Federated Learning via Synthetic Data

Reference 22

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E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Communication-efficient federated learning based on compressed sensing,

Reference 23

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E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing FedSynth: Gradient Compression via Synthetic Data in Federated Learning

Reference 24

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E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Sparse Communication for Distributed Gradient Descent

Reference 25

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E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing z-signfedavg: A unified stochastic sign-based compression for federated learning,

Reference 26

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E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Optimization methods for large- scale machine learning,

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E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Deep leakage from gradients,

Reference 28

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E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Sparsified sgd with memory,

Reference 29

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E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing A theoretical study of dataset distillation,

Reference 30

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E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 31

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E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing The convergence of sparsified gradient methods,

Reference 32

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E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Communication- efficient federated learning with single-step synthetic features compres- sor for faster convergence,

Reference 33

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E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Communication-efficient federated learning,

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E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Rethinking gradient sparsification as total error minimiza- tion,

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E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Pre- dicting parameters in deep learning,

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E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing 1-bit stochastic gradient descent and its application to data-parallel distributed training of speech dnns,

Reference 37

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E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Communication-efficient federated learning via knowledge distillation,

Reference 38

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E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Communication-efficient federated deep learning with layerwise asynchronous model update and temporally weighted aggregation,

Reference 39

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E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing A convergence theory for deep learn- ing via over-parameterization,

Reference 40

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E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing A systematic review on overfitting control in shallow and deep neural networks,

Reference 41

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E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing iDLG: Improved Deep Leakage from Gradients

Reference 42

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E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Gradient leakage attacks in federated learning: Research frontiers, taxonomy and future directions,

Reference 43

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E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing The mnist database of handwritten digit images for machine learning research,

Reference 44

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E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 45

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E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Emnist: Extending mnist to handwritten letters,

Reference 46

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E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Learning multiple layers of features from tiny images,

Reference 47

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E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Character-level convolutional net- works for text classification,

Reference 48

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E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Maximum likelihood estimation of dirichlet distribution parameters,

Reference 49

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E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Tackling the objective inconsistency problem in heterogeneous federated optimiza- tion,

Reference 50

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This paper cites Federated learning on non-iid data silos: An experimental study,.

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Federated learning on non-iid data silos: An experimental study,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T06:02:19.643303Z

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Observation 4ac4e0b3-bfbd-43c8-bc72-a51ace22f156 · outbound

This paper cites Deep residual learning for image recognition,.

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Deep residual learning for image recognition,

Reference 52

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Observation 79f58300-6356-4d0b-8a35-859b5ce08f5e · outbound

This paper cites Designing network design spaces,.

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Designing network design spaces,

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-09T06:02:19.268907Z

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Observation 9d7c34d5-18e5-4136-bb0e-0dcfcc3568d4 · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift,.

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Batch normalization: Accelerating deep network training by reducing internal covariate shift,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T06:02:19.607985Z

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Observation 91301748-0a49-481e-89ef-022d55ba642f · outbound

This paper cites Dropout: a simple way to prevent neural networks from over- fitting,.

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Dropout: a simple way to prevent neural networks from over- fitting,

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-09T06:02:19.278671Z

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This paper cites Why Batch Normalization Damage Federated Learning on Non-IID Data?.

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Why Batch Normalization Damage Federated Learning on Non-IID Data?

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-08-09T06:02:19.382181Z

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Observation 937c0ac2-97ee-4006-8089-a38300759cd4 · outbound

This paper cites Personalized federated learning with moreau envelopes,.

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Personalized federated learning with moreau envelopes,

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-09T06:02:19.288186Z

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Observation 2464d4cb-119e-4d3a-8fb5-3d57cb7dbd4c · outbound

This paper cites Federated cinn clustering for accurate clustered federated learning,.

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Federated cinn clustering for accurate clustered federated learning,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T06:02:19.571115Z

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Observation ff11bd00-46eb-4aba-a029-91c486bf1242 · outbound

This paper cites Step-ahead error feedback for distributed training with compressed gradient,.

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Step-ahead error feedback for distributed training with compressed gradient,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T06:02:19.555675Z

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Observation 5325efdb-1fca-4bf1-b06d-07555f7c2e97 · outbound

This paper cites Detached error feedback for distributed sgd with random sparsification,.

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Detached error feedback for distributed sgd with random sparsification,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T06:02:19.539719Z

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Observation 1e10963c-02c2-4a66-8a54-6f1245d838d7 · outbound

This paper cites Federated Reinforcement Learning: Techniques, Applications, and Open Challenges.

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Federated Reinforcement Learning: Techniques, Applications, and Open Challenges

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-09T06:02:19.306937Z

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Observation 99dde919-f6a0-4b64-a445-8cf1aae320c6 · outbound

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E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-08-09T06:02:19.525436Z

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Observation 098ac7d8-94b0-41ba-9e45-f49a871f1977 · outbound

This paper cites an unresolved cited work.

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-09T06:02:19.510921Z

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Pith citing papers

Observation f69c06de-620b-42ed-a305-0f0d9bc6863f · inbound

Discerning and quantifying high frequency activities in EEG under normal and epileptic conditions cites this paper.

Discerning and quantifying high frequency activities in EEG under normal and epileptic conditions E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing

Reference 2025

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T19:21:36.674749Z

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