Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence | Paper Citation Record

Observation 0951e76c-7340-42d9-bcc7-8fa6ea00d7bc · outbound

This paper cites GPT-4 Technical Report.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence GPT-4 Technical Report

Reference 1

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This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Gemini: A Family of Highly Capable Multimodal Models

Reference 2

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This paper cites Deepseek-inspired exploration of rl-based llms and synergy with wire- less networks: A survey,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Deepseek-inspired exploration of rl-based llms and synergy with wire- less networks: A survey,

Reference 3

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Observation a3a40a20-f27b-4a46-96ee-814eb778f70c · outbound

This paper cites Chatgpt: Enabling human-like conversations and shaping the future of language processing,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Chatgpt: Enabling human-like conversations and shaping the future of language processing,

Reference 4

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This paper cites Evaluating text-to-visual generation with image-to-text generation,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Evaluating text-to-visual generation with image-to-text generation,

Reference 5

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This paper cites Empirical evaluation of chatgpt on requirements information retrieval under zero-shot setting,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Empirical evaluation of chatgpt on requirements information retrieval under zero-shot setting,

Reference 6

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This paper cites Mp-fedcl: Multiprototype federated contrastive learning for edge intelligence,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Mp-fedcl: Multiprototype federated contrastive learning for edge intelligence,

Reference 7

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Observation 329a3c26-9ccb-4709-99e1-ba1b216fde3c · outbound

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

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Communication-efficient learning of deep networks from decentralized data,

Reference 8

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Observation 2fd2ae3a-2b16-445b-8157-63092a306321 · outbound

This paper cites Federated learning for healthcare: Systematic review and architecture proposal,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Federated learning for healthcare: Systematic review and architecture proposal,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:42.333846Z

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Observation 308b56d6-a667-4e17-90be-34cf996ab094 · outbound

This paper cites Federated learning for open banking,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Federated learning for open banking,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:42.321662Z

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Observation 7f33eb0c-fc07-4fe1-9113-e314b161c4ef · outbound

This paper cites Perturbation-enabled deep federated learning for preserving internet of things-based social net- works,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Perturbation-enabled deep federated learning for preserving internet of things-based social net- works,

Reference 11

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This paper cites Intriguing properties of neural networks.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Intriguing properties of neural networks

Reference 12

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This paper cites Explaining and harnessing adversarial examples,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Explaining and harnessing adversarial examples,

Reference 13

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Observation af8f6fd5-0482-40d2-bb43-8730d8b7d9aa · outbound

This paper cites Fat: Federated adversarial training,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Fat: Federated adversarial training,

Reference 14

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Observation 8855fad7-1531-425c-9417-d362bc0e4b63 · outbound

This paper cites Federated robustness propagation: sharing adversarial robustness in heterogeneous federated learning,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Federated robustness propagation: sharing adversarial robustness in heterogeneous federated learning,

Reference 15

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This paper cites Delving into the adversarial robustness of federated learning,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Delving into the adversarial robustness of federated learning,

Reference 16

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This paper cites Logit calibration and feature contrast for robust federated learning on non-iid data,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Logit calibration and feature contrast for robust federated learning on non-iid data,

Reference 17

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Observation 7af5c9ba-cc53-4e43-aaeb-2e95e65dadf3 · outbound

This paper cites Calfat: Calibrated federated ad- versarial training with label skewness,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Calfat: Calibrated federated ad- versarial training with label skewness,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:42.234172Z

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Observation a2b24796-e2d8-47c1-a70b-627b8564c38a · outbound

This paper cites Towards robust federated learning via logits calibration on non-iid data,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Towards robust federated learning via logits calibration on non-iid data,

Reference 19

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Observation 0c7926f5-4538-4082-b9dc-0ff50ec0c6f8 · outbound

This paper cites Fedccl: Federated dual-clustered feature contrast under domain heterogeneity,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Fedccl: Federated dual-clustered feature contrast under domain heterogeneity,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:42.208912Z

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Observation 09451af3-dcec-45cc-a5d9-0572d0e2dce5 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Distilling the Knowledge in a Neural Network

Reference 21

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Observation cd00aaab-99c3-4986-8007-1ec901e9a9c1 · outbound

This paper cites Knowledge distillation: A good teacher is patient and consistent,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Knowledge distillation: A good teacher is patient and consistent,

Reference 22

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Observation 07efdc24-71ce-42e2-a460-7be46ee29a55 · outbound

This paper cites Adversarially robust distillation,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Adversarially robust distillation,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:42.185272Z

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Observation fb5186df-aa4a-4a58-a033-34af8fe4e2ad · outbound

This paper cites Revisiting adversarial robust- ness distillation: Robust soft labels make student better,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Revisiting adversarial robust- ness distillation: Robust soft labels make student better,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:42.170400Z

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Observation 679c1915-0f67-4a55-8616-20d84ac6d029 · outbound

This paper cites Boosting accuracy and robustness of student models via adaptive adversarial distillation,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Boosting accuracy and robustness of student models via adaptive adversarial distillation,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:42.154935Z

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Observation 410eb0af-b642-4c26-b045-6e76bfb79822 · outbound

This paper cites Adversarial training for free!,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Adversarial training for free!,

Reference 26

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Observation 472b5f4d-e4ad-4818-91a3-9ba1d38c8664 · outbound

This paper cites Recent advances in adversarial training for adversarial robustness,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Recent advances in adversarial training for adversarial robustness,

Reference 27

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This paper cites Towards deep learning models resistant to adversarial attacks,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Towards deep learning models resistant to adversarial attacks,

Reference 28

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Observation c8fe95f0-77c8-4dcb-83e3-6570ffa52499 · outbound

This paper cites Theoretically principled trade-off between robustness and accuracy,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Theoretically principled trade-off between robustness and accuracy,

Reference 29

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This paper cites Continuous multivariate distributions,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Continuous multivariate distributions,

Reference 30

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This paper cites Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks,

Reference 31

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This paper cites Federated optimization in heterogeneous networks,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Federated optimization in heterogeneous networks,

Reference 32

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This paper cites Model-contrastive federated learning,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Model-contrastive federated learning,

Reference 33

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Observation c01f597e-81e2-482b-a226-a6aacc096498 · outbound

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

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification

Reference 34

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This paper cites Federated Learning with Personalization Layers.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Federated Learning with Personalization Layers

Reference 35

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This paper cites Fedproto: Federated prototype learning across heterogeneous clients,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Fedproto: Federated prototype learning across heterogeneous clients,

Reference 36

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Observation 855e1eb9-723b-4970-9797-920a4e56b135 · outbound

This paper cites A framework for multi-prototype based federated learning: Towards the edge intelligence,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence A framework for multi-prototype based federated learning: Towards the edge intelligence,

Reference 37

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Observation fa428125-3e16-44f4-871e-42079e69420f · outbound

This paper cites Efficient parameter-free clustering using first neighbor relations,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Efficient parameter-free clustering using first neighbor relations,

Reference 38

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Observation 916c2f48-d3f3-4ae6-9bf9-1b744fa354e8 · outbound

This paper cites Federated learning with label distribution skew via logits calibration,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Federated learning with label distribution skew via logits calibration,

Reference 39

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Observation b9a7c2db-7a66-4258-8952-18d9ab9ecc4b · outbound

This paper cites Rethinking Client Drift in Federated Learning: A Logit Perspective.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Rethinking Client Drift in Federated Learning: A Logit Perspective

Reference 40

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This paper cites Data-free knowledge distillation for het- erogeneous federated learning,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Data-free knowledge distillation for het- erogeneous federated learning,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:41.987473Z

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Observation 24f17ce8-07a1-4455-92ce-f6ba5d85a6a5 · outbound

This paper cites Dfrd: Data-free ro- bustness distillation for heterogeneous federated learning,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Dfrd: Data-free ro- bustness distillation for heterogeneous federated learning,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:41.973630Z

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Observation 8a0b591c-d04e-4c66-b5c6-c35c32884e65 · outbound

This paper cites Logit standardization in knowledge distillation,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Logit standardization in knowledge distillation,

Reference 43

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Observation e0069fff-deef-4363-899b-908922f20197 · outbound

This paper cites Differentiable feature aggregation search for knowledge distillation,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Differentiable feature aggregation search for knowledge distillation,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:41.943192Z

Source-reported events for the cited work

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Observation 3889f348-b575-485c-ba59-43e30ab9da13 · outbound

This paper cites Data-free knowledge distillation via feature exchange and activation region constraint,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Data-free knowledge distillation via feature exchange and activation region constraint,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:41.930669Z

Source-reported events for the cited work

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Observation 6b69a072-e76a-4a97-861c-c2994cdb1563 · outbound

This paper cites Probabilistic knowledge transfer for lightweight deep representation learning,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Probabilistic knowledge transfer for lightweight deep representation learning,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:41.916328Z

Source-reported events for the cited work

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Observation 1bc2c4ad-9190-4191-9a66-ecf72bda0946 · outbound

This paper cites Pairwise difference relational distillation for object re-identification,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Pairwise difference relational distillation for object re-identification,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:41.904220Z

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Observation 98c2e626-1f0b-4720-8f15-0d7eeb1e3df1 · outbound

This paper cites Ensemble distillation for robust model fusion in federated learning,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Ensemble distillation for robust model fusion in federated learning,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:41.890501Z

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Observation 41830cb1-1c94-4b29-acef-05a20ff896fe · outbound

This paper cites Data-free knowledge filtering and distillation in federated learning,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Data-free knowledge filtering and distillation in federated learning,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:41.878454Z

Source-reported events for the cited work

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Observation ae6d91f1-c632-4555-b137-58c9af616fea · outbound

This paper cites Knowledge distillation in federated learning: Where and how to distill?,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Knowledge distillation in federated learning: Where and how to distill?,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:41.863475Z

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Observation 37e787d8-e5c9-410d-b9dd-0797f74ccf12 · outbound

This paper cites Prototype Helps Federated Learning: Towards Faster Convergence.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Prototype Helps Federated Learning: Towards Faster Convergence

Reference 51

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Source-reported events for the cited work

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Observation 6476b965-9393-424f-b07a-55a9291af0e3 · outbound

This paper cites Federated learning from pre-trained models: A contrastive learning approach,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Federated learning from pre-trained models: A contrastive learning approach,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:41.849340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 352d0742-9544-45a3-90d1-ad5c6c1edcbf · outbound

This paper cites Data-Free Adversarial Distillation.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Data-Free Adversarial Distillation

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-10T14:31:41.286721Z

Source-reported events for the cited work

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Observation a0054070-7ce1-4441-b2d4-b986e3a67819 · outbound

This paper cites Knowledge distillation assisted robust federated learning: Towards edge intelli- gence,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Knowledge distillation assisted robust federated learning: Towards edge intelli- gence,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:41.837609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a5e674ad-38db-48c3-a896-9b1ecdaac9f3 · outbound

This paper cites Does physical ad- versarial example really matter to autonomous driving? towards system- level effect of adversarial object evasion attack,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Does physical ad- versarial example really matter to autonomous driving? towards system- level effect of adversarial object evasion attack,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:41.826739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a86873a6-3297-4b0b-a0d5-601b0040668c · outbound

This paper cites Adversarial examples are not easily detected: Bypassing ten detection methods,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Adversarial examples are not easily detected: Bypassing ten detection methods,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:41.814346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 03f03a2b-b39f-45ae-876e-b29caf2a6278 · outbound

This paper cites Robustness of SAM: Segment Anything Under Corruptions and Beyond.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Robustness of SAM: Segment Anything Under Corruptions and Beyond

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-10T14:31:41.302251Z

Source-reported events for the cited work

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Observation ac4901b7-317f-455c-a4e0-ac21e33da83a · outbound

This paper cites Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-10T14:31:41.306000Z

Source-reported events for the cited work

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Observation 579d910a-f4e4-4dd3-95fb-4dc0e47c2462 · outbound

This paper cites Adversarial examples in the physical world,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Adversarial examples in the physical world,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:41.801998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d43114bf-d1d1-4a34-a5dc-f3b05e91b716 · outbound

This paper cites Square attack: a query-efficient black-box adversarial attack via random search,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Square attack: a query-efficient black-box adversarial attack via random search,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:41.790791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8ccf6033-587a-41e9-800f-3cde84c730ee · outbound

This paper cites Towards evaluating the robustness of neural networks,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Towards evaluating the robustness of neural networks,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:41.778451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 95319510-dfba-4708-8094-49af37823ce9 · outbound

This paper cites Univer- sal adversarial perturbations,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Univer- sal adversarial perturbations,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:41.764913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 34264503-f1b9-4d2a-869a-5a3e2f7dda0a · outbound

This paper cites The limitations of adversarial training and the blind-spot attack,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence The limitations of adversarial training and the blind-spot attack,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:41.753216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 294cc635-f905-4b1f-9ddf-d324d9c3f517 · outbound

This paper cites Bayesian nonparametric federated learning of neural networks,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Bayesian nonparametric federated learning of neural networks,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:41.739929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2d1ba1c3-572b-446b-840b-af5550a64e67 · outbound

This paper cites On the robustness of the cvpr 2018 white- box adversarial example defenses,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence On the robustness of the cvpr 2018 white- box adversarial example defenses,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:41.727806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e93909e4-1d13-4f3d-ac7e-45e517d3efda · outbound

This paper cites mixup: Beyond empirical risk minimization,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence mixup: Beyond empirical risk minimization,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:41.716875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 38404c2e-4650-4a70-845b-3afc0c51a9cb · outbound

This paper cites Kullback– leibler divergence metric learning,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Kullback– leibler divergence metric learning,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:41.705405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 33c5e9be-9d30-4a6c-a435-10698206ebee · outbound

This paper cites Adversarial Logit Pairing.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Adversarial Logit Pairing

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-10T14:31:41.344045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7daaf68-3c02-4033-bfd5-3531ca1db7ab · outbound

This paper cites Evaluating and understanding the robustness of adversarial logit pairing,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Evaluating and understanding the robustness of adversarial logit pairing,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:41.694671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 69e53379-976a-4ec6-bc91-0764339b550a · outbound

This paper cites Adaptive adversarial logits pairing,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Adaptive adversarial logits pairing,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:41.683339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 74a27759-1f32-4598-a5de-333ce368a4ea · outbound

This paper cites Improving adversarial robustness requires revisiting misclassified examples,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Improving adversarial robustness requires revisiting misclassified examples,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:41.671961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ee858b19-98de-4778-ae7a-a2da4400f5d3 · outbound

This paper cites Gradient-based learning applied to document recognition,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Gradient-based learning applied to document recognition,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:41.658140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4b0e2cbd-ff99-4ee1-98be-d77dd03d4ccf · outbound

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

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Learning multiple layers of features from tiny images,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:41.645750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f9f0b736-7b0f-4bbd-acfc-3ae23f49e66c · outbound

This paper cites Wide residual networks,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Wide residual networks,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:41.632094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 13861747-f44d-44b3-9061-e64569326e8f · outbound

This paper cites Fixing Data Augmentation to Improve Adversarial Robustness.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Fixing Data Augmentation to Improve Adversarial Robustness

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-10T14:31:41.367035Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2f56aa9-c813-4c0a-8a9f-7bad02011ad6 · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-10T14:31:41.371024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3aac882-4e96-4a5e-af87-3333d16532c9 · outbound

This paper cites Deep residual learning for image recognition,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Deep residual learning for image recognition,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:41.618609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c93c6c07-9517-4a08-81d2-40fc0f398dd6 · outbound

This paper cites Knowledge distillation: A survey,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Knowledge distillation: A survey,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:41.605965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f24254b1-f2a1-452a-85ce-793361675b09 · outbound

This paper cites Improving robustness using generated data,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Improving robustness using generated data,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:41.594114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ffc67910-e366-4e0b-b1d5-55cf2795ed54 · outbound

This paper cites Revisiting residual networks for adversarial robustness,.

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence Revisiting residual networks for adversarial robustness,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:31:41.580924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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