FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix | Paper Citation Record

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This paper cites Baffle: Backdoor detection via feedback-based federated learning.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Baffle: Backdoor detection via feedback-based federated learning

Reference 1

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This paper cites How To Backdoor Federated Learning.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix How To Backdoor Federated Learning

Reference 2

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

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix A little is enough: Circumventing defenses for distributed learning

Reference 3

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This paper cites Analyzing feder- ated learning through an adversarial lens.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Analyzing feder- ated learning through an adversarial lens

Reference 4

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This paper cites Machine learn- ing with adversaries: Byzantine tolerant gradient descent.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Machine learn- ing with adversaries: Byzantine tolerant gradient descent

Reference 5

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This paper cites Understanding distributed poisoning attack in federated learning.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Understanding distributed poisoning attack in federated learning

Reference 6

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Observation 18a2b53e-abff-4f02-a7d8-442ea81372d0 · outbound

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

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping

Reference 7

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This paper cites Mpaf: Model poisoning attacks to federated learning based on fake clients.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Mpaf: Model poisoning attacks to federated learning based on fake clients

Reference 9

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Observation a51a2da2-a76b-47c9-8f09-85333185c066 · outbound

This paper cites Towards multi-party targeted model poisoning attacks against federated learning systems.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Towards multi-party targeted model poisoning attacks against federated learning systems

Reference 10

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Observation f6904867-0918-4a2b-9a7f-7784c8b0ebb8 · outbound

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

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Local model poisoning attacks to {Byzantine-Robust} federated learning

Reference 11

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This paper cites Do We Really Need to Design New Byzantine-robust Aggregation Rules?.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Do We Really Need to Design New Byzantine-robust Aggregation Rules?

Reference 12

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This paper cites Badnets: Evaluating backdooring attacks on deep neural networks.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Badnets: Evaluating backdooring attacks on deep neural networks

Reference 13

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

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix The hidden vulnerability of distributed learning in byzantium

Reference 14

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This paper cites Flmjr: Improving robustness of federated learning via model stability.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Flmjr: Improving robustness of federated learning via model stability

Reference 15

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This paper cites Deep residual learning for image recognition.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Deep residual learning for image recognition

Reference 16

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This paper cites Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing

Reference 17

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This paper cites Scaffold: Stochastic controlled averaging for federated learning.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Scaffold: Stochastic controlled averaging for federated learning

Reference 18

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

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Learning multiple layers of features from tiny images

Reference 19

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This paper cites A review of applications in federated learning.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix A review of applications in federated learning

Reference 20

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This paper cites Federated optimization in heterogeneous networks.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Federated optimization in heterogeneous networks

Reference 21

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This paper cites On the convergence of fedavg on non-iid data, 2020.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix On the convergence of fedavg on non-iid data, 2020

Reference 22

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

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Communication-efficient learning of deep networks from decentralized data

Reference 23

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This paper cites Local and Central Differential Privacy for Robustness and Privacy in Federated Learning.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Local and Central Differential Privacy for Robustness and Privacy in Federated Learning

Reference 24

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This paper cites Reading digits in natural images with unsupervised feature learning.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Reading digits in natural images with unsupervised feature learning

Reference 25

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This paper cites {FLAME}: Taming backdoors in federated learning.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix {FLAME}: Taming backdoors in federated learning

Reference 26

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This paper cites Defending against backdoors in federated learning with robust learning rate.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Defending against backdoors in federated learning with robust learning rate

Reference 27

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This paper cites Robust aggregation for federated learning.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Robust aggregation for federated learning

Reference 28

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

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Manipulating the byzantine: Optimizing model poisoning attacks and defenses for federated learning

Reference 29

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

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Back to the drawing board: A critical evaluation of poisoning attacks on production federated learning

Reference 30

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This paper cites Can You Really Backdoor Federated Learning?.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Can You Really Backdoor Federated Learning?

Reference 31

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This paper cites Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere

Reference 32

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This paper cites Crfl: Certifiably robust federated learning against backdoor attacks.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Crfl: Certifiably robust federated learning against backdoor attacks

Reference 33

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This paper cites Dba: Distributed backdoor attacks against federated learning.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Dba: Distributed backdoor attacks against federated learning

Reference 34

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This paper cites Model Poisoning Attacks to Federated Learning via Multi-Round Consistency.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Model Poisoning Attacks to Federated Learning via Multi-Round Consistency

Reference 35

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This paper cites Fedrola: Robust federated learning against model poisoning via layer-based aggregation.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Fedrola: Robust federated learning against model poisoning via layer-based aggregation

Reference 36

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

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Byzantine-robust distributed learning: Towards optimal statistical rates

Reference 37

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This paper cites Fedredefense: Defending against model poisoning attacks for federated learning using model update reconstruction error.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Fedredefense: Defending against model poisoning attacks for federated learning using model update reconstruction error

Reference 38

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This paper cites Fldetector: Defending federated learning against model poisoning attacks via detecting malicious clients.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Fldetector: Defending federated learning against model poisoning attacks via detecting malicious clients

Reference 39

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This paper cites Backdoor Federated Learning by Poisoning Backdoor-Critical Layers.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Backdoor Federated Learning by Poisoning Backdoor-Critical Layers

Reference 40

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This paper cites FedGraM has shown similar performances with C = 20% and C = 30%.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix FedGraM has shown similar performances with C = 20% and C = 30%

Reference 41

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This paper cites Under all kinds of untargeted attacks, it can successfully defend the attacks and maintain the test accuracy of the global model at a high level.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Under all kinds of untargeted attacks, it can successfully defend the attacks and maintain the test accuracy of the global model at a high level

Reference 42

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This paper cites SVHN is an easier classification task compared with CIFAR10 which further facilitate the robustness of FedGraM.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix SVHN is an easier classification task compared with CIFAR10 which further facilitate the robustness of FedGraM

Reference 43

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This paper cites [Yes] " is generally preferable to.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix [Yes] " is generally preferable to

Reference 44

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FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper

Reference 45

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FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Limitations

Reference 46

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FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Guidelines: • The answer NA means that the paper does not include theoretical results

Reference 47

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FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Guidelines: • The answer NA means that the paper does not include experiments

Reference 48

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FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Guidelines: • The answer NA means that paper does not include experiments requiring code

Reference 49

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FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Guidelines: • The answer NA means that the paper does not include experiments

Reference 50

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FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Guidelines: • The answer NA means that the paper does not include experiments

Reference 51

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FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Guidelines: • The answer NA means that the paper does not include experiments

Reference 52

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FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics

Reference 53

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3d843db6-c018-4f54-869a-9f91bf09db1b · outbound

This paper cites Guidelines: • The answer NA means that there is no societal impact of the work performed.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Guidelines: • The answer NA means that there is no societal impact of the work performed

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:30.780603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 05fb2787-cdf5-47c0-b16d-064d4e4e4afe · outbound

This paper cites an unresolved cited work.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:43:30.615861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f4c93266-ac6a-4e40-b0f0-dfcaed0319ab · outbound

This paper cites Guidelines: • The answer NA means that the paper does not use existing assets.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Guidelines: • The answer NA means that the paper does not use existing assets

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:30.408458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0801d23e-9580-4721-81c0-8e6c55a3eed0 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not release new assets.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Guidelines: • The answer NA means that the paper does not release new assets

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T15:43:24.960573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01a0581b-9f9c-4ac3-9c62-166b24de0332 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:30.124947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 08f09d19-d22c-4e20-b441-6d848b94ce51 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:25.908406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 72c2e98b-2de2-4034-8504-7d3216cd373f · outbound

This paper cites Answer: [NA] Justification: The core method development in this research does not involve LLMs as any important, original, or non-standard components.

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix Answer: [NA] Justification: The core method development in this research does not involve LLMs as any important, original, or non-standard components

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T15:43:25.494823Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers