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

Observation 63509926-7e2d-4989-9ac7-86895e216e20 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:38.634861Z

Source-reported events for the cited work

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

Observation 126fd1c1-5425-4abe-b5a9-4ea768723bc9 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-07T15:43:18.557553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 661640f1-172f-4f1b-9914-fa7ee3858f32 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:38.324752Z

Source-reported events for the cited work

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

Observation be79534b-1f77-4de9-aaa9-7ece9a2e453e · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:38.106423Z

Source-reported events for the cited work

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

Observation e03d196d-1ce1-4878-9210-6936a965f745 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:37.832976Z

Source-reported events for the cited work

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

Observation 52b4edcd-201b-4298-b18e-6d5e88e14792 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:37.436737Z

Source-reported events for the cited work

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

Observation 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

Resolution: unresolved; no resolver link, observed 2026-08-07T15:43:19.169271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9738aa72-ea0f-4aa4-a083-e94c8e4bdb36 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:37.142437Z

Source-reported events for the cited work

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

Observation 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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:36.903248Z

Source-reported events for the cited work

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

Observation 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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:36.645900Z

Source-reported events for the cited work

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

Observation f2fade37-6539-4816-987c-4b93406357b7 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-07T15:43:19.864924Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa4e3c00-2386-479d-b6d4-22921f127d27 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:36.417058Z

Source-reported events for the cited work

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

Observation 919a327f-9c77-4c8c-b9bb-d1e61f54196d · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-07T15:43:20.104688Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a229f86-7a93-4760-80a5-43f8c6306c65 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:36.229202Z

Source-reported events for the cited work

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

Observation bff77564-cb56-45d6-be6e-a87b816ed28b · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-07T15:43:20.389062Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f878b71a-8ab8-4927-9d0e-88c6e3e38174 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-07T15:43:20.514911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97b1ad61-a869-421d-9b17-b38df67b330d · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-07T15:43:20.635751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f709f6b-f9ec-45d1-96dc-d5240c566eaa · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-07T15:43:20.701172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b527716-3fa6-4530-9518-bbfa07206b11 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-07T15:43:20.804827Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6d6a22a-f847-4953-ae00-ceab638f3f7c · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-07T15:43:20.934371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c928820a-abfc-4d59-9373-3eec66f891cf · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:35.871835Z

Source-reported events for the cited work

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

Observation 01f4b404-8cd6-4ae1-8a47-c3c4acb8cabd · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-07T15:43:21.175704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07e7bc06-2564-4ffa-be51-0066316c1f48 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-07T15:43:21.295807Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7882a72-db62-4f87-9e2c-f45f05e944d4 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:35.626943Z

Source-reported events for the cited work

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

Observation 30562c3e-addc-4fbf-bc0a-82acb46285fe · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:35.420155Z

Source-reported events for the cited work

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

Observation 569ebf6f-44ba-4539-9042-30754827f1d1 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:35.234335Z

Source-reported events for the cited work

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

Observation bacc66cc-dfed-42fc-b2bd-a1d153061c93 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-07T15:43:21.711437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e95f9f46-c67e-4628-83e0-440c90503c63 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:35.093168Z

Source-reported events for the cited work

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

Observation 77cda690-b1bc-409f-a5f1-f547b8a4fa2e · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:34.931824Z

Source-reported events for the cited work

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

Observation 1bc21286-cd58-4436-96f6-9e0c93b91e43 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-07T15:43:22.015946Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b121c8c2-8415-43be-8b89-da4054cc194c · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-07T15:43:22.127451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a69bde9-4ad2-47f9-b9ea-881c19eb9024 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:34.736644Z

Source-reported events for the cited work

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

Observation fa5920d5-3a3c-459f-a1ab-3ffa776ef676 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:34.485338Z

Source-reported events for the cited work

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

Observation 0dfe9d95-d911-4352-9c4b-b63b4e46883e · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-07T15:43:22.436920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14b4d9a5-cbde-4b40-8a8f-6de6434b7f5d · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:34.268415Z

Source-reported events for the cited work

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

Observation 7b334183-3bfc-4bdb-8527-736e5099bb99 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:34.076305Z

Source-reported events for the cited work

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

Observation 85da89d9-b013-4815-8055-fd9846b99042 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:33.910001Z

Source-reported events for the cited work

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

Observation 43ec8b2e-5622-41f9-9ae9-cb2edd940ac3 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:33.762241Z

Source-reported events for the cited work

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

Observation b87a9dde-ff07-4175-88d2-66836ce42fcf · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-07T15:43:22.934842Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 980ffa7c-92b1-4409-a119-e304b902f27a · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:33.596137Z

Source-reported events for the cited work

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

Observation db9ae580-9d6b-440d-8b04-a1734a078c7c · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:33.383514Z

Source-reported events for the cited work

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

Observation 44c6f57e-805a-42c4-9cc7-f221833a2b1b · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:33.165971Z

Source-reported events for the cited work

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

Observation ce82ad4a-72e7-4cd1-87a1-1420adddac70 · outbound

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

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T15:43:32.937462Z

Source-reported events for the cited work

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

Observation a02cdb79-49d1-4564-935c-6b5f987a230b · outbound

This paper cites Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:32.672637Z

Source-reported events for the cited work

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

Observation dbb15add-1ef3-4511-85af-cdcfb32c8bbf · outbound

This paper cites Limitations.

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

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:32.438093Z

Source-reported events for the cited work

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

Observation ad9cdaf7-ba04-4d82-804a-9f688300daec · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include theoretical results.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:32.240694Z

Source-reported events for the cited work

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

Observation 91dc93d2-d817-4658-812e-fc3e03e95875 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:31.972044Z

Source-reported events for the cited work

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

Observation 2dd82a55-f2f3-4f64-966f-03b540625b47 · outbound

This paper cites Guidelines: • The answer NA means that paper does not include experiments requiring code.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:31.829684Z

Source-reported events for the cited work

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

Observation d39ebdcc-7410-4256-afa0-6c37651444be · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:31.643699Z

Source-reported events for the cited work

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

Observation 0357809f-44b0-4d28-9192-2ec68369b8f4 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:31.481143Z

Source-reported events for the cited work

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

Observation e6df1490-1ccb-434d-957e-36e5ed0205be · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:31.252275Z

Source-reported events for the cited work

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

Observation 980e1f65-e211-43bf-9672-05d339551ab5 · outbound

This paper cites Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:43:30.995909Z

Source-reported events for the cited work

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

Observation 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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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