Paper Citation Record · LEDGER

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks

As of 19 August 2026, this Paper Citation Record lists 79 of 79 outbound references and 2 inbound Pith citation observations for arXiv:2412.19354.

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

pith.paper-citation-record.v1
2412.19354 v1

Coverage vector

measured 79 of 79 reference resolution

Typed states for the displayed outbound observations.

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measured 81 of 81 standing notices

One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:31:41.306000Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T15:34:57.732409Z

Reference resolution

79 of 79 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fbae8af7-8414-4109-a7c6-74eaa64210fb · outbound

This paper cites Edge intelligence: The confluence of edge computing and artificial intelligence,.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Edge intelligence: The confluence of edge computing and artificial intelligence,

Reference 1

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This paper cites Edge comput- ing with artificial intelligence: A machine learning perspective,.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Edge comput- ing with artificial intelligence: A machine learning perspective,

Reference 2

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

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Node selection toward faster convergence for federated learning on non-iid data,

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This paper cites Nvm-enhanced machine learning inference in 6g edge computing,.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Nvm-enhanced machine learning inference in 6g edge computing,

Reference 4

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This paper cites Wireless powered mobile edge computing networks: A survey,.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Wireless powered mobile edge computing networks: A survey,

Reference 5

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Observation 5eea231f-46d4-4a8f-ac4f-dca057274c83 · outbound

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

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Mp-fedcl: Multiprototype federated contrastive learning for edge intelligence,

Reference 6

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This paper cites Convergence of edge computing and deep learning: A comprehensive survey,.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Convergence of edge computing and deep learning: A comprehensive survey,

Reference 7

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This paper cites Model aggregation techniques in federated learning: A comprehensive survey,.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Model aggregation techniques in federated learning: A comprehensive survey,

Reference 8

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

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Communication-efficient learning of deep networks from decentralized data,

Reference 9

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This paper cites Fedbn: Federated learning on non-iid features via local batch normalization,.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Fedbn: Federated learning on non-iid features via local batch normalization,

Reference 10

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Observation a04c3e4e-4bb9-4896-963a-45444ea8593c · outbound

This paper cites Fat: Federated adversarial training,.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Fat: Federated adversarial training,

Reference 11

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This paper cites Federated robustness propaga- tion: sharing adversarial robustness in heterogeneous federated learning,.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Federated robustness propaga- tion: sharing adversarial robustness in heterogeneous federated learning,

Reference 12

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

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Privacy and robustness in federated learning: Attacks and defenses,

Reference 13

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Observation 805f7241-3576-4e9f-8af3-5d51ce3ae705 · outbound

This paper cites Logit Calibration and Feature Contrast for Robust Federated Learning on Non-IID Data.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Logit Calibration and Feature Contrast for Robust Federated Learning on Non-IID Data

Reference 14

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Observation 87c9d4e3-9abd-47d9-a265-66673de49b14 · outbound

This paper cites A survey on security and privacy issues in modern healthcare systems: Attacks and defenses,.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks A survey on security and privacy issues in modern healthcare systems: Attacks and defenses,

Reference 15

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Observation 001ebb1c-84e5-447d-9a5f-0db2279f6cb8 · outbound

This paper cites Ef- fective adversarial examples identification of credit card transactions,.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Ef- fective adversarial examples identification of credit card transactions,

Reference 16

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Observation 9dbcb674-64e1-47a7-a62a-6a97f128680e · outbound

This paper cites Explaining and harnessing adversarial examples,.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Explaining and harnessing adversarial examples,

Reference 17

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Observation 2b7bb53b-c264-45ee-a631-88500bac6b76 · outbound

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

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Towards deep learning models resistant to adversarial attacks,

Reference 18

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Observation 813f2785-ed4a-4c9f-bc47-2b7aa3451f6a · outbound

This paper cites Federated adversarial learning: A framework with convergence analysis,.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Federated adversarial learning: A framework with convergence analysis,

Reference 19

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Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks,

Reference 20

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Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Model-contrastive federated learning,

Reference 21

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Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Federated class-incremental learning with dynamic feature extractor fusion,

Reference 22

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

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Knowledge distillation assisted robust federated learning: Towards edge intelli- gence,

Reference 23

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Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Gradient-based learning applied to document recognition,

Reference 24

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Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

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Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Reading digits in natural images with unsupervised feature learning,

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Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Geodesic flow kernel for unsupervised domain adaptation,

Reference 27

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Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Learning multiple layers of features from tiny images,

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Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Handling both stragglers and adversaries for robust federated learning,

Reference 29

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Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Fe- dat: a high-performance and communication-efficient federated learning system with asynchronous tiers,

Reference 30

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This paper cites Cdfed: Contribution-based dynamic federated learning for managing system and statistical heterogeneity,.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Cdfed: Contribution-based dynamic federated learning for managing system and statistical heterogeneity,

Reference 31

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Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Federated learning with sparsified model perturbation: Improving accuracy under client-level differential privacy,

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Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Representative kernels-based cnn for faster transmission in federated learning,

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Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks The analysis and optimization of volatile clients in over-the-air federated learning,

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Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Scaffold: Stochastic controlled averaging for federated learn- ing,

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Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks FedMEKT: Distillation-based Embedding Knowledge Transfer for Multimodal Federated Learning

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Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Fedproto: Federated prototype learning across heterogeneous clients,

Reference 37

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Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Personalized federated learning with moreau envelopes,

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Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Multi-level personalized feder- ated learning on heterogeneous and long-tailed data,

Reference 39

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Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Federated optimization in heterogeneous networks,

Reference 40

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Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Fedfed: Feature distillation against data heterogeneity in federated learning,

Reference 41

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This paper cites An aggregation-free federated learning for tackling data heterogeneity,.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks An aggregation-free federated learning for tackling data heterogeneity,

Reference 42

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Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Dfrd: Data-free robustness distillation for heterogeneous federated learning,

Reference 43

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This paper cites Distilling the Knowledge in a Neural Network.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Distilling the Knowledge in a Neural Network

Reference 44

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Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Logit standardization in knowledge distillation,

Reference 45

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

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Differentiable feature aggregation search for knowledge distillation,

Reference 46

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Observation e968ae0f-fad1-4664-98f4-03798479b2aa · outbound

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

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Data-free knowledge distillation via feature exchange and activation region constraint,

Reference 47

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Observation e0cd431d-c987-48fd-bb55-7d6604d8abcd · outbound

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

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Probabilistic knowledge transfer for lightweight deep representation learning,

Reference 48

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Observation 2c54fb1c-d7a6-4971-b5e7-540b360b7d1f · outbound

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

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Pairwise difference relational distillation for object re-identification,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:44:59.621348Z

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Observation 39c80b32-0d7e-4118-adc2-99dc3956e85d · outbound

This paper cites Knowledge distillation and student-teacher learning for visual intelligence: A review and new outlooks,.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Knowledge distillation and student-teacher learning for visual intelligence: A review and new outlooks,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:44:59.607739Z

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Observation c02637c3-e886-426d-a37e-b1e7bbfb736c · outbound

This paper cites Federated Distillation: A Survey.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Federated Distillation: A Survey

Reference 51

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Observation 493a1a1e-c61f-475e-abd7-8edf9aaf470b · outbound

This paper cites Federated learning with label-masking distillation,.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Federated learning with label-masking distillation,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:44:59.595461Z

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Observation d7388bb6-761b-46c8-a824-1ac5e8559297 · outbound

This paper cites Data-Free Adversarial Distillation.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Data-Free Adversarial Distillation

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-11T00:44:59.083110Z

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Observation d2948649-22d7-4834-b697-d79d465baac8 · outbound

This paper cites Adversarially robust distillation,.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Adversarially robust distillation,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:44:59.584549Z

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Observation 999dec77-77e6-4003-ae4d-aef0b2878e1a · outbound

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

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Delving into the adversarial robustness of federated learning,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:44:59.573418Z

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Observation 285bb77c-b7dd-42b2-93ec-e7d16a8a54d0 · outbound

This paper cites Continuous multivariate distributions,.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Continuous multivariate distributions,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:44:59.563082Z

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Observation 75723d57-8e46-4e9e-9182-d9edaea4994a · outbound

This paper cites Intriguing properties of neural networks.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Intriguing properties of neural networks

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-11T00:44:59.098536Z

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Observation 79bb39a2-ea79-4060-bd36-f265b9f4ca46 · outbound

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

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Towards robust federated learning via logits calibration on non-iid data,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:44:59.553596Z

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Observation 0df04547-fd24-4341-9c65-ec5113e9fa6d · outbound

This paper cites Adversarial examples in the physical world,.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Adversarial examples in the physical world,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:44:59.543421Z

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Observation e69ee11e-d884-40fa-afab-14f70e73f4e7 · outbound

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

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Square attack: a query-efficient black-box adversarial attack via random search,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:44:59.532937Z

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Observation 5b049118-2427-4220-bf8b-8a3b3fce6cce · outbound

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

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Towards evaluating the robustness of neural networks,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:44:59.521414Z

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Observation 52d9938d-0f5b-4598-910f-12afa7166505 · outbound

This paper cites Data augmentation can improve robustness,.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Data augmentation can improve robustness,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:44:59.508892Z

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Observation 1c74985f-c6e3-45f3-ae7f-657af7a1c4e2 · outbound

This paper cites Maximum-entropy adver- sarial data augmentation for improved generalization and robustness,.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Maximum-entropy adver- sarial data augmentation for improved generalization and robustness,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:44:59.498044Z

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Observation b20139ce-1e91-4eff-8da6-3a1831f6aecc · outbound

This paper cites Towards robustness of deep neural networks via regularization,.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Towards robustness of deep neural networks via regularization,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:44:59.486506Z

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Observation 7c4056e4-462c-4621-a6cd-b21bbae32cac · outbound

This paper cites Improving dnn robustness to adversarial attacks using jacobian regularization,.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Improving dnn robustness to adversarial attacks using jacobian regularization,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:44:59.474915Z

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Observation e4079a89-2dab-4790-b566-6069b816b0cb · outbound

This paper cites Advances in adversarial attacks and defenses in computer vision: A survey,.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Advances in adversarial attacks and defenses in computer vision: A survey,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:44:59.462988Z

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Observation 727b6321-a780-45ba-9f10-d437314dc67b · outbound

This paper cites On adversarial robustness: A neural architecture search perspective,.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks On adversarial robustness: A neural architecture search perspective,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:44:59.450024Z

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Observation 2ac09c4a-5537-4eee-b138-ee44fd50144b · outbound

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

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks The limitations of adversarial training and the blind-spot attack,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:44:59.437351Z

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Observation 2459ff05-8378-4296-a457-b8a577dac6ac · outbound

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

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Calfat: Calibrated federated ad- versarial training with label skewness,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:44:59.425872Z

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Observation 9e124d30-a264-403e-a0d7-31c3eafbc55b · outbound

This paper cites On the Robustness of the CVPR 2018 White-Box Adversarial Example Defenses.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks On the Robustness of the CVPR 2018 White-Box Adversarial Example Defenses

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-11T00:44:59.151554Z

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Observation 03f59a9a-f964-4408-b062-2ee0880169a7 · outbound

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

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Fedccl: Federated dual-clustered feature contrast under domain heterogeneity,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:44:59.412840Z

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Observation edaf937e-a1b3-441d-bb5f-bd56a3c82742 · outbound

This paper cites Ensemble federated learning with non-iid data in wireless networks,.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Ensemble federated learning with non-iid data in wireless networks,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:44:59.401576Z

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Observation 9e1dd134-ea58-47f9-9d0a-3bdad4b82645 · outbound

This paper cites Fraug: Tackling federated learning with non-iid features via representation augmenta- tion,.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Fraug: Tackling federated learning with non-iid features via representation augmenta- tion,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:44:59.389678Z

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Observation 1a98bb23-e8c6-4abf-96bd-e478587a6702 · outbound

This paper cites Fedproc: Prototypical contrastive federated learning on non-iid data,.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Fedproc: Prototypical contrastive federated learning on non-iid data,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:44:59.377077Z

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

Observation 2f7fec41-f7cb-4688-b2e6-5de0d7095272 · outbound

This paper cites Adversarial Logit Pairing.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Adversarial Logit Pairing

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-11T00:44:59.171677Z

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Observation 22f866d4-63c0-48fc-8b6b-fd519dc15485 · outbound

This paper cites Adversarial vertex mixup: Toward better adversarially robust generalization,.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Adversarial vertex mixup: Toward better adversarially robust generalization,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:44:59.364792Z

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Observation 91495a22-33c6-4238-9172-a33c0d409325 · outbound

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

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Theoretically principled trade-off between robustness and accuracy,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:44:59.352910Z

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Observation 503bdca1-e535-446e-916c-3a45800e57c9 · outbound

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

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks Bayesian nonparametric federated learning of neural networks,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:44:59.340893Z

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

Observation d661226f-31ff-40d3-b8b5-6b9e189818f2 · outbound

This paper cites V-measure: A conditional entropy- based external cluster evaluation measure,.

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks V-measure: A conditional entropy- based external cluster evaluation measure,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:44:59.329353Z

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Pith citing papers

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

Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence cites this paper.

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

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Observation 1a104bbc-4c84-4ab1-a449-68fd9ca21931 · inbound

A Survey on Foundation Models for Personalized Federated Intelligence cites this paper.

A Survey on Foundation Models for Personalized Federated Intelligence Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-22T15:34:57.734230Z

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