Paper Citation Record · LEDGER

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning

As of 10 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 2 inbound Pith citation observations for arXiv:2502.03801.

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

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2502.03801 v1

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measured 59 of 59 reference resolution

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Reference resolution

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Outbound references

Observation caa862d7-3831-4790-a90d-e52bb910a1da · outbound

This paper cites Accessed: 2024-10-03.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Accessed: 2024-10-03

Reference 1

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This paper cites Accessed: 2024-10-03.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Accessed: 2024-10-03

Reference 2

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Observation 36a72f15-83b0-4d44-85dd-9e676778c33f · outbound

This paper cites How to backdoor federated learning.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning How to backdoor federated learning

Reference 3

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Observation 56b6101a-93f3-4216-93bc-20719b8bbbcb · outbound

This paper cites A little is enough: Circumventing defenses for distributed learning.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning A little is enough: Circumventing defenses for distributed learning

Reference 4

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Observation 267eba6f-ef9a-4814-ae57-f0b958cf1ffd · outbound

This paper cites Analyzing federated learn- ing through an adversarial lens.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Analyzing federated learn- ing through an adversarial lens

Reference 5

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Observation 2e965971-e8a6-4006-8587-4a304374ec60 · outbound

This paper cites Poi- soning attacks against support vector machines.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Poi- soning attacks against support vector machines

Reference 6

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Observation e43dd710-5d52-4e31-b9bf-4382161470d1 · outbound

This paper cites Machine learning with adver- saries: Byzantine tolerant gradient descent.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Machine learning with adver- saries: Byzantine tolerant gradient descent

Reference 7

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Observation b7b81a7c-30f4-4602-904a-b29924c08e4e · outbound

This paper cites FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping

Reference 8

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This paper cites Asynchronous byzantine machine learning (the case of sgd).

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Asynchronous byzantine machine learning (the case of sgd)

Reference 9

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Observation 5e891a49-be58-4e05-a592-89a43bf6f7d4 · outbound

This paper cites Utilization of fate in risk management of credit in small and micro enterprises, 2020.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Utilization of fate in risk management of credit in small and micro enterprises, 2020

Reference 10

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Observation 8c2ecbe4-2ddd-49ea-9403-2e32c4d6358d · outbound

This paper cites Local model poisoning attacks to {Byzantine- Robust} federated learning.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Local model poisoning attacks to {Byzantine- Robust} federated learning

Reference 11

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Observation 9e59f55f-2424-4eec-bccd-06daf1ed1453 · outbound

This paper cites The limitations of federated learning in sybil set- tings.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning The limitations of federated learning in sybil set- tings

Reference 12

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This paper cites Backdoor attacks and defenses in federated learning: State-of-the-art, taxonomy, and future direc- tions.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Backdoor attacks and defenses in federated learning: State-of-the-art, taxonomy, and future direc- tions

Reference 13

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Observation 961ec327-e2ca-48b1-8fe2-c6a6e4786e9c · outbound

This paper cites BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain

Reference 14

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This paper cites The hidden vulnerability of distributed learning in byzantium.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning The hidden vulnerability of distributed learning in byzantium

Reference 15

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This paper cites Federated Learning for Mobile Keyboard Prediction.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Federated Learning for Mobile Keyboard Prediction

Reference 16

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This paper cites Deep Residual Learning for Image Recognition.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Deep Residual Learning for Image Recognition

Reference 17

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Observation 52e40c0e-b6bb-444a-910d-b4b981f43e8b · outbound

This paper cites Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification

Reference 18

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This paper cites Robust statis- tics.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Robust statis- tics

Reference 19

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This paper cites Communication-efficient dis- tributed sgd with sketching.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Communication-efficient dis- tributed sgd with sketching

Reference 20

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This paper cites Learning from history for byzantine robust optimiza- tion.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Learning from history for byzantine robust optimiza- tion

Reference 21

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This paper cites Byzantine-robust learning on heterogeneous datasets via bucketing.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Byzantine-robust learning on heterogeneous datasets via bucketing

Reference 22

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This paper cites Learning mul- tiple layers of features from tiny images.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Learning mul- tiple layers of features from tiny images

Reference 23

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This paper cites Ardis: a swedish historical handwritten digit dataset.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Ardis: a swedish historical handwritten digit dataset

Reference 24

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SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Gradient-based learning applied to document recognition

Reference 25

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SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Gradient-based learning applied to document recognition

Reference 26

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This paper cites 3dfed: Adaptive and extensible framework for covert backdoor attack in federated learning.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning 3dfed: Adaptive and extensible framework for covert backdoor attack in federated learning

Reference 27

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This paper cites An experimental study of byzantine-robust aggregation schemes in federated learning.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning An experimental study of byzantine-robust aggregation schemes in federated learning

Reference 28

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This paper cites Federated learning in mobile edge networks: A comprehensive survey.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Federated learning in mobile edge networks: A comprehensive survey

Reference 29

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This paper cites Privacy and robustness in federated learning: Attacks and defenses.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Privacy and robustness in federated learning: Attacks and defenses

Reference 30

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

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Communication-efficient learning of deep networks from decentralized data

Reference 31

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This paper cites Feder- ated learning for internet of things: A comprehensive survey.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Feder- ated learning for internet of things: A comprehensive survey

Reference 32

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Observation 17d6d000-d4cd-4b16-90f0-9d8defe2adaf · outbound

This paper cites Federated learning for smart healthcare: A survey.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Federated learning for smart healthcare: A survey

Reference 33

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This paper cites In 31st USENIX Security Sympo- sium (USENIX Security 22) , pages 1415–1432, 2022.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning In 31st USENIX Security Sympo- sium (USENIX Security 22) , pages 1415–1432, 2022

Reference 34

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This paper cites Federated evaluation and tuning for on-device personalization: System de- sign & applications, 2022.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Federated evaluation and tuning for on-device personalization: System de- sign & applications, 2022

Reference 35

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This paper cites Robust aggregation for federated learning.IEEE Trans- actions on Signal Processing, 70:1142–1154, 2022.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Robust aggregation for federated learning.IEEE Trans- actions on Signal Processing, 70:1142–1154, 2022

Reference 36

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This paper cites Federated Learning for Emoji Prediction in a Mobile Keyboard.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Federated Learning for Emoji Prediction in a Mobile Keyboard

Reference 37

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This paper cites Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Kone ˇcný, Sanjiv Kumar, and Hugh Brendan McMahan.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Kone ˇcný, Sanjiv Kumar, and Hugh Brendan McMahan

Reference 38

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This paper cites DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection

Reference 39

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This paper cites Sok: Systematizing attack studies in federated learning–from sparseness to completeness.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Sok: Systematizing attack studies in federated learning–from sparseness to completeness

Reference 40

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Observation 4ca53f48-7a24-4d29-b0d9-4fbeab2498ef · outbound

This paper cites Manipulating the byzantine: Optimizing model poisoning attacks and defenses for federated learning.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Manipulating the byzantine: Optimizing model poisoning attacks and defenses for federated learning

Reference 41

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Observation 8685fcd5-7d04-4d00-beb5-091f879d4521 · outbound

This paper cites Back to the drawing board: A critical evaluation of poisoning attacks on production federated learning.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Back to the drawing board: A critical evaluation of poisoning attacks on production federated learning

Reference 42

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This paper cites Au- ror: Defending against poisoning attacks in collabora- tive deep learning systems.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Au- ror: Defending against poisoning attacks in collabora- tive deep learning systems

Reference 43

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Observation 0c34ac9b-062c-45f2-9c6e-cb86f2a46a92 · outbound

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SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Sparsified sgd with memory

Reference 44

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Observation cf2f4e1d-4d8e-41af-b84d-94b82fc955bd · outbound

This paper cites Can You Really Backdoor Federated Learning?.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Can You Really Backdoor Federated Learning?

Reference 45

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This paper cites A compre- hensive survey on poisoning attacks and countermea- sures in machine learning.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning A compre- hensive survey on poisoning attacks and countermea- sures in machine learning

Reference 46

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Observation f01ffae3-68f6-4067-a955-0932d986859d · outbound

This paper cites Data and model poisoning backdoor attacks on wireless federated learn- ing, and the defense mechanisms: A comprehensive survey.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Data and model poisoning backdoor attacks on wireless federated learn- ing, and the defense mechanisms: A comprehensive survey

Reference 47

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Observation 4f9409f7-923e-45f0-b74e-67c8afe3edfd · outbound

This paper cites Attack of the tails: Yes, you really can backdoor federated learn- ing.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Attack of the tails: Yes, you really can backdoor federated learn- ing

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:45:23.776035Z

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Observation 07c1508c-06ce-4236-bc17-7b2dbef81dbe · outbound

This paper cites Federated Learning with Matched Averaging.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Federated Learning with Matched Averaging

Reference 49

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Observation cc04af7f-d0cc-44ab-83b4-71ce50f18545 · outbound

This paper cites Crfl: Certifiably robust federated learning against back- door attacks.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Crfl: Certifiably robust federated learning against back- door attacks

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:45:23.761033Z

Source-reported events for the cited work

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Observation 28c1f131-35b9-4fe3-b407-c386d36b0d1a · outbound

This paper cites Dba: Distributed backdoor attacks against federated learn- ing.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Dba: Distributed backdoor attacks against federated learn- ing

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:45:23.745465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 56318e87-f848-43e2-8d85-2398b31953bf · outbound

This paper cites Fall of empires: Breaking byzantine-tolerant sgd by in- ner product manipulation.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Fall of empires: Breaking byzantine-tolerant sgd by in- ner product manipulation

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:45:23.729048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f63dd423-c4c2-4e05-9e6d-2289113658a2 · outbound

This paper cites Byzantine-robust Federated Learning through Collaborative Malicious Gradient Filtering.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Byzantine-robust Federated Learning through Collaborative Malicious Gradient Filtering

Reference 53

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Observation bcf6aa56-8a22-4bee-af21-ea2413744101 · outbound

This paper cites Heterogeneous federated learning: State- of-the-art and research challenges.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Heterogeneous federated learning: State- of-the-art and research challenges

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:45:23.711572Z

Source-reported events for the cited work

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Observation f3ad6e4d-377b-47b0-93ea-15afba1ee3d5 · outbound

This paper cites Jailbreak Attacks and Defenses Against Large Language Models: A Survey.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Jailbreak Attacks and Defenses Against Large Language Models: A Survey

Reference 55

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Observation 87e975f4-206c-4a99-8a4e-b2ba566cea8e · outbound

This paper cites Byzantine-robust distributed learning: Towards optimal statistical rates.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Byzantine-robust distributed learning: Towards optimal statistical rates

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:45:23.693865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 98b4b011-f0d0-4f1c-9b7c-861a21d4c878 · outbound

This paper cites Bayesian nonparametric federated learning of neural networks.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Bayesian nonparametric federated learning of neural networks

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:45:23.678175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cf43b1bb-c255-4393-a875-73457b28d351 · outbound

This paper cites Fldetector: Defending federated learn- ing against model poisoning attacks via detecting mali- cious clients.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning Fldetector: Defending federated learn- ing against model poisoning attacks via detecting mali- cious clients

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:45:23.662069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 257b4679-642d-49e3-82ba-00d7cc148a4c · outbound

This paper cites batchrun.py.

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning batchrun.py

Reference 59

Resolution: malformed identifier; raw_fallback, observed 2026-08-09T00:45:23.644754Z

Source-reported events for the cited work

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Pith citing papers

Observation 0fee46c0-1ac6-4e71-9109-2513216b2db3 · inbound

FLARE: Adaptive Multi-Dimensional Reputation for Robust Client Reliability in Federated Learning cites this paper.

FLARE: Adaptive Multi-Dimensional Reputation for Robust Client Reliability in Federated Learning SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-05-17T20:10:10.954312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 88335609-46cf-4e7f-ac89-9de7938219af · inbound

Graph Representation Learning Augmented Model Manipulation on Federated Fine-Tuning of LLMs cites this paper.

Graph Representation Learning Augmented Model Manipulation on Federated Fine-Tuning of LLMs SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning

Reference 49

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