Paper Citation Record · LEDGER

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions

As of 17 August 2026, this Paper Citation Record lists 100 of 268 outbound references and 11 inbound Pith citation observations for arXiv:2411.12377.

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

pith.paper-citation-record.v1
2411.12377 v2

Coverage vector

measured 100 of 268 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T17:39:18.459913Z

measured 111 of 111 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:52:02.256519Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T21:58:58.243453Z

Reference resolution

100 of 268 outbound references displayed

verified exact6
verified fuzzy0
unresolved94
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 79b81cc0-4f0a-4ffa-87a6-2f18225c9363 · outbound

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

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Communication-efficient learning of deep networks from decentral- ized data,

Reference 1

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Observation 8a0ed0ee-1162-473e-9b6a-192103567aa7 · outbound

This paper cites A survey for federated learning evaluations: Goals and measures,.

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions A survey for federated learning evaluations: Goals and measures,

Reference 2

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Observation 582d7af5-9346-44fc-b9ae-391ae33d1f2e · outbound

This paper cites Gdpr: Reshaping the landscape of digital transformation and business strategy,.

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Gdpr: Reshaping the landscape of digital transformation and business strategy,

Reference 3

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Observation 1b9149ff-21fb-4137-a886-d07eb0a3c9e2 · outbound

This paper cites Complying with hipaa and hitech,.

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Complying with hipaa and hitech,

Reference 4

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Observation 49c29d9b-6724-4074-bb76-7ea85963539d · outbound

This paper cites Application of federated learning techniques for arrhythmia classification using 12-lead ecg signals,.

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Application of federated learning techniques for arrhythmia classification using 12-lead ecg signals,

Reference 5

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Observation 4c790696-b629-4c80-a342-8520b2042e59 · outbound

This paper cites Transparency and privacy: the role of explainable ai and federated learning in financial fraud detection,.

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Transparency and privacy: the role of explainable ai and federated learning in financial fraud detection,

Reference 6

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Observation 4cb67460-45e4-438f-a1d9-e6f9b8e12ae6 · outbound

This paper cites Fedasa: A personalized federated learning with adaptive model aggregation for heterogeneous mobile edge computing,.

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Fedasa: A personalized federated learning with adaptive model aggregation for heterogeneous mobile edge computing,

Reference 7

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Observation 0a71a5eb-52b0-41c9-afe8-8d6787afcbbb · outbound

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

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Learning multiple layers of features from tiny images,

Reference 8

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Observation 9a9f19e6-eb5f-4e35-a9cc-d379d5060ab3 · outbound

This paper cites Mnist hand- written digit database,.

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Mnist hand- written digit database,

Reference 9

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Observation 1ea9f682-7be5-4dff-9df2-c1e44997aec7 · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 10

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Observation 6862e343-12f0-469c-96ed-14ceec9713ee · outbound

This paper cites Enhancing generalization in federated learning with heterogeneous data: A comparative literature review,.

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Enhancing generalization in federated learning with heterogeneous data: A comparative literature review,

Reference 11

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Observation 5b0307e2-5221-4320-8dd9-5b1cd06681fd · outbound

This paper cites Federated learning with non-iid data: A survey,.

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Federated learning with non-iid data: A survey,

Reference 12

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Observation adb680e3-8903-455f-87bb-5e8cf31d8d1e · outbound

This paper cites A review of federated learning methods in heterogeneous scenarios,.

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions A review of federated learning methods in heterogeneous scenarios,

Reference 13

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Observation 76fc1ff4-747c-411a-88a6-daf47317eeeb · outbound

This paper cites Advances in Robust Federated Learning: A Survey with Heterogeneity Considerations.

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Advances in Robust Federated Learning: A Survey with Heterogeneity Considerations

Reference 14

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Observation b0eade91-ae6a-47e5-9450-1d54539b3437 · outbound

This paper cites Non-iid data and continual learning processes in federated learning: A long road ahead,.

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Non-iid data and continual learning processes in federated learning: A long road ahead,

Reference 15

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Observation 70d468dc-9f36-42b6-89c3-a4598d712439 · outbound

This paper cites A state-of-the-art survey on solving non-iid data in federated learning,.

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions A state-of-the-art survey on solving non-iid data in federated learning,

Reference 16

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Observation 53ee4ce7-b877-4d67-87c9-4df3f26d704a · outbound

This paper cites Federated learning on non-iid data: A survey,.

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Federated learning on non-iid data: A survey,

Reference 17

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Observation 353334d2-af8a-4dee-855b-02ad55d270f3 · outbound

This paper cites Decentralized federated averaging,.

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Decentralized federated averaging,

Reference 18

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Observation 85b5521d-246a-4615-9abb-39e26132afe3 · outbound

This paper cites Federated Learning: Opportunities and Challenges.

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Federated Learning: Opportunities and Challenges

Reference 19

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Observation 21dd0a19-8809-4f0d-85ef-d21b87e0c43e · outbound

This paper cites Cross-Silo Federated Learning: Challenges and Opportunities.

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Cross-Silo Federated Learning: Challenges and Opportunities

Reference 20

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Observation 491feb2b-8e27-4f72-abcb-464dbcf33412 · outbound

This paper cites Federated learning for internet of things: A comprehensive survey,.

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Federated learning for internet of things: A comprehensive survey,

Reference 21

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Observation 87eeb499-2f5a-4a59-8eb4-3edc3923d704 · outbound

This paper cites Client-edge-cloud hierarchical federated learning,.

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Client-edge-cloud hierarchical federated learning,

Reference 22

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Observation e34b377f-0954-403d-ae0a-92aef0e2940f · outbound

This paper cites A survey on federated learning,.

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions A survey on federated learning,

Reference 23

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Observation a2e4c138-e6ee-4b74-934e-5e7e217ef44e · outbound

This paper cites Federated learning review: Fundamentals, enabling technologies, and future applications,.

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Federated learning review: Fundamentals, enabling technologies, and future applications,

Reference 24

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Observation 33f37153-2fd9-48d5-b394-eb2a0b4902a0 · outbound

This paper cites Vertical federated learning: Concepts, advances, and challenges,.

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Vertical federated learning: Concepts, advances, and challenges,

Reference 25

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Observation 8492fbff-8dbb-49a9-afcf-80e97ef7c41c · outbound

This paper cites BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning.

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning

Reference 26

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Observation c33f09f8-f80a-4da6-80b6-1f0e31061799 · outbound

This paper cites Federated Learning with Non-IID Data.

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Federated Learning with Non-IID Data

Reference 27

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This paper cites A review: Data pre- processing and data augmentation techniques,.

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions A review: Data pre- processing and data augmentation techniques,

Reference 28

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This paper cites Resampling strategies for imbalanced regression: a survey and empirical analysis,.

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Resampling strategies for imbalanced regression: a survey and empirical analysis,

Reference 29

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This paper cites Evaluating machine learning models and their diagnostic value,.

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Evaluating machine learning models and their diagnostic value,

Reference 30

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This paper cites Internet of things intrusion detection: Centralized, on-device, or federated learning?.

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Internet of things intrusion detection: Centralized, on-device, or federated learning?

Reference 31

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This paper cites FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data.

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data

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This paper cites Node selection toward faster convergence for federated learning on non-iid data,.

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Node selection toward faster convergence for federated learning on non-iid data,

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

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Federated learning on non-iid data silos: An experimental study,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions SemiFed: Semi-supervised Federated Learning with Consistency and Pseudo-Labeling

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Client selection in federated learning under imperfections in environment,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Evaluating the commu- nication efficiency in federated learning algorithms,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions On safeguarding privacy and security in the framework of federated learning,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Poisoning attacks in federated learning: A survey,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Addressing heterogeneity in federated learning with client selection via submodular optimization,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Wscc: A weight-similarity- based client clustering approach for non-iid federated learning,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions A contribution-based device selection scheme in federated learning,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Towards federated learning: An overview of methods and applications,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions How to do a systematic review: a best practice guide for conducting and reporting narrative reviews, meta-analyses, and meta-syntheses,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions (2004) Google scholar

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions (2000) Ieee xplore

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions (2004) Scopus

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions (1997) Web of science

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Fedel: Federated ensemble learning for non-iid data,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Aedfl: efficient asynchronous decentralized federated learning with heterogeneous devices,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Federated learning with a balanced heterogeneous-yoke and loose restriction,

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This paper cites On the effectiveness of partial variance reduction in federated learning with heterogeneous data,.

Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions On the effectiveness of partial variance reduction in federated learning with heterogeneous data,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Tackling data heterogeneity in federated learning with class prototypes,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Label-efficient self-supervised federated learning for tackling data heterogeneity in medical imaging,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Feder- ated learning with label distribution skew via logits calibration,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Fedpd: A federated learning framework with adaptivity to non-iid data,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Ferrari: A personalized federated learning framework for heterogeneous edge clients,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Federated learning with taskonomy for non-iid data,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Per- sonalized cross-silo federated learning on non-iid data,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Exploiting label skews in federated learning with model concatenation,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Flgan: Gan-based unbiased federated learning under non-iid settings,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Ringsfl: An adaptive split federated learning towards taming client heterogeneity,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Byzantine-robust variance- reduced federated learning over distributed non-iid data,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Lfighter: Defending against the label-flipping attack in federated learning,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Cross- to-merge training with class balance strategy for learning with noisy labels,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Blockchain-based fairness- enhanced federated learning scheme against label flipping attack,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Fedcir: Client- invariant representation learning for federated non-iid features,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversity

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Fairfed: Enabling group fairness in federated learning,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Fairness and accuracy in horizontal federated learning,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Decentralized federated learning of deep neural networks on non-iid data

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Robust federated learning: The case of affine distribution shifts,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Fairtrade: Achieving pareto-optimal trade-offs between balanced accuracy and fairness in federated learning,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Fraug: Tackling federated learning with non-iid features via representation augmenta- tion,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Clustered federated learning with adaptive local differential privacy on heterogeneous iot data,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Federated learning for dis- tribution skewed data using sample weights,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Federated learning on non-iid graphs via structural knowledge sharing,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions A Simple Data Augmentation for Feature Distribution Skewed Federated Learning

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Fl-enhance: A federated learning framework for balancing non-iid data with aug- mented and shared compressed samples,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Cross-silo federated learning for multi-tier networks with vertical and horizontal data partitioning,

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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions Fedmultimodal: A benchmark for multimodal federated learning,

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Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T02:41:17.228267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7f5d33ee-1fd9-4bc9-9a7a-cddde85d37bd · inbound

BESplit: Bias-Compensated Split Federated Learning with Evidential Aggregation cites this paper.

BESplit: Bias-Compensated Split Federated Learning with Evidential Aggregation Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-20T14:28:21.185575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 983792d9-7157-4f4a-9186-08603eb461b0 · inbound

AlignFed: Alignment-Aware Asynchronous Federated Fine-Tuning for Large Language Models in Heterogeneous Edge Environments cites this paper.

AlignFed: Alignment-Aware Asynchronous Federated Fine-Tuning for Large Language Models in Heterogeneous Edge Environments Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T21:17:24.956943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d2b9acd5-96a3-4388-82f9-1243cf3fbb3d · inbound

Entropy-Regularized Probabilistic Gates for Sparse Model Discovery in Scarce-Data Federated Learning cites this paper.

Entropy-Regularized Probabilistic Gates for Sparse Model Discovery in Scarce-Data Federated Learning Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-07-02T19:27:18.633389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation eab8db7e-eb97-47f4-9859-17932070a184 · inbound

Benchmarking Federated Learning and Knowledge Distillation for Point Cloud Classification cites this paper.

Benchmarking Federated Learning and Knowledge Distillation for Point Cloud Classification Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-07-03T21:58:58.245623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 916dc3ef-bbf1-40f8-a953-0d1cf145ddf8 · inbound

PIcsC: Partitioning-Induced Covariate Shift Correction cites this paper.

PIcsC: Partitioning-Induced Covariate Shift Correction Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T02:30:36.253345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 606089d8-7a43-491e-91c5-326c009cd949 · inbound

On the Effectiveness of Adaptation Strategies for VLM-Based Federated Learning in Remote Sensing cites this paper.

On the Effectiveness of Adaptation Strategies for VLM-Based Federated Learning in Remote Sensing Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:05.008101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.