FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices | Paper Citation Record

Observation 52fdb395-02ff-485b-a1af-23f373d608f8 · outbound

This paper cites Topology- aware federated learning in edge computing: A comprehensive survey,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Topology- aware federated learning in edge computing: A comprehensive survey,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:00.387016Z

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Observation a8ccccfe-b2d4-46cf-a3c9-3d587690e7d4 · outbound

This paper cites Decentralized federated learning with intermediate results in mobile edge computing,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Decentralized federated learning with intermediate results in mobile edge computing,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:00.370182Z

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Observation 85f340de-cfde-4d8b-8dcb-22af3cf0ce3b · outbound

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

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Communication-efficient learning of deep networks from decentralized data,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:00.352608Z

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Observation f0a826f4-aad0-49ef-9b85-2fce87b25355 · outbound

This paper cites Advances and open problems in federated learning,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Advances and open problems in federated learning,

Reference 4

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Observation a756608f-f0b8-41c6-bbed-345ac83f376e · outbound

This paper cites A Field Guide to Federated Optimization.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices A Field Guide to Federated Optimization

Reference 5

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Observation 532cf1ed-6b57-4cdd-b69a-ff30ef69f541 · outbound

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

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Federated learning for healthcare: Systematic review and architecture proposal,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:00.319336Z

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Observation b175d163-ab15-4f63-8c9e-05bb554442bd · outbound

This paper cites Fine-grained preference-aware personalized federated poi recommendation with data sparsity,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Fine-grained preference-aware personalized federated poi recommendation with data sparsity,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:00.303190Z

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Observation 1c7915d7-ec87-4aa1-bb3d-e310c6ef35c4 · outbound

This paper cites Federated meta-learning for fraudulent credit card detection,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Federated meta-learning for fraudulent credit card detection,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:00.287390Z

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Observation 6aa8315f-c479-4604-8fbc-f321bd920dfa · outbound

This paper cites Communication-efficient federated learning via knowledge distillation,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Communication-efficient federated learning via knowledge distillation,

Reference 9

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Observation b2aba5b7-e874-4ce7-914a-2729dd88577a · outbound

This paper cites Feddm: Iterative distribution matching for communication-efficient federated learning,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Feddm: Iterative distribution matching for communication-efficient federated learning,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:00.261617Z

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Observation fd5dd5c1-dc08-45f3-9b28-d4029cb80142 · outbound

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

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Privacy and robustness in federated learning: Attacks and defenses,

Reference 11

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Observation 9adbaeb6-b674-4430-b56d-0b7e738e9414 · outbound

This paper cites Efficient privacy-preserving federated learning under dishonest-majority setting,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Efficient privacy-preserving federated learning under dishonest-majority setting,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:00.234990Z

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Observation 773fc175-4117-4d60-a769-820949bb6bd1 · outbound

This paper cites Man-in-the-middle attacks against machine learning classifiers via malicious generative models,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Man-in-the-middle attacks against machine learning classifiers via malicious generative models,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:00.219664Z

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Observation e62169d0-e8d2-4ec5-9710-46a0567640b1 · outbound

This paper cites See through gradients: Image batch recovery via gradinversion,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices See through gradients: Image batch recovery via gradinversion,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:00.204443Z

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Observation ababb9a1-6781-4676-b677-b3d4ce1c3363 · outbound

This paper cites One-Shot Federated Learning.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices One-Shot Federated Learning

Reference 15

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Observation 9ad8614b-4d5a-45dc-b81d-edd2e7a68fb1 · outbound

This paper cites Model decomposition and reassembly for purified knowledge transfer in personalized federated learning,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Model decomposition and reassembly for purified knowledge transfer in personalized federated learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:00.189314Z

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Observation 17d57eed-e452-4202-b4a8-38112bbb8ac4 · outbound

This paper cites Achieving linear speedup in asynchronous federated learning with heterogeneous clients,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Achieving linear speedup in asynchronous federated learning with heterogeneous clients,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:00.173329Z

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Observation 6720b805-5afc-4e41-aa11-46e8c846a0bb · outbound

This paper cites Flrce: Resource-efficient federated learning with early-stopping strategy,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Flrce: Resource-efficient federated learning with early-stopping strategy,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:00.157596Z

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Observation daf3fb4f-45e0-47c0-82b9-1c8f5659c8f9 · outbound

This paper cites Dense: Data-free one-shot federated learning,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Dense: Data-free one-shot federated learning,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:00.141814Z

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Observation ea4a7696-a338-4dc1-ba21-cba431533620 · outbound

This paper cites Data-free one-shot feder- ated learning under very high statistical heterogeneity,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Data-free one-shot feder- ated learning under very high statistical heterogeneity,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:00.126133Z

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Observation ae2bf4ed-24cd-4fcb-8c5d-2708b2f04ca2 · outbound

This paper cites Distilled One-Shot Federated Learning.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Distilled One-Shot Federated Learning

Reference 21

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Observation 5d7ed159-80f9-4e88-9bfa-df44cb28a2ec · outbound

This paper cites Dataset Distillation.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Dataset Distillation

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-08T04:53:58.950067Z

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Observation 945634b1-aa6c-4ae1-996a-8dbdf0782e94 · outbound

This paper cites Practical one-shot federated learning for cross-silo setting,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Practical one-shot federated learning for cross-silo setting,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:00.110754Z

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Observation f36cceda-842f-433f-b4a4-e1f4f12425c1 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Distilling the Knowledge in a Neural Network

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-08T04:53:58.959873Z

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Observation 84b520a3-bd66-421c-a877-1074859c06e5 · outbound

This paper cites Does knowledge distillation really work?.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Does knowledge distillation really work?

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:00.095631Z

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Observation 8c7c295c-f039-4ef9-b815-e1332ea2cf10 · outbound

This paper cites Enhancing one-shot federated learning through data and ensemble co-boosting,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Enhancing one-shot federated learning through data and ensemble co-boosting,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:00.079722Z

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Observation 0ca0ac92-0410-45fb-beb8-c516ce87ad58 · outbound

This paper cites Learning structured output representation using deep conditional generative models,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Learning structured output representation using deep conditional generative models,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:00.063190Z

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Observation 7be44317-f496-449b-bdef-2134b0451908 · outbound

This paper cites Open set learning with counterfactual images,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Open set learning with counterfactual images,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:00.047065Z

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Observation 8c9c3b61-e209-4eb8-afff-7643f0733cc5 · outbound

This paper cites Learning placeholders for open- set recognition,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Learning placeholders for open- set recognition,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:00.029004Z

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Observation 52d2b009-15f3-4608-85e5-37a3f4de99c4 · outbound

This paper cites One-shot Federated Learning via Synthetic Distiller-Distillate Communication.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices One-shot Federated Learning via Synthetic Distiller-Distillate Communication

Reference 30

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Observation 30b622ee-a6bd-458b-9a3b-0b28878553fd · outbound

This paper cites Expanding the Reach of Federated Learning by Reducing Client Resource Requirements.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Expanding the Reach of Federated Learning by Reducing Client Resource Requirements

Reference 31

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Observation 7abecfb3-3a54-4ce9-b913-d5de8e76f8b9 · outbound

This paper cites Dropout: a simple way to prevent neural networks from overfitting,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Dropout: a simple way to prevent neural networks from overfitting,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:00.002011Z

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Observation 082cd888-ef1c-4bc8-93fb-3dfce30da08d · outbound

This paper cites Heterofl: Computation and com- munication efficient federated learning for heterogeneous clients,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Heterofl: Computation and com- munication efficient federated learning for heterogeneous clients,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:53:59.985768Z

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Observation 04fe57ea-90c8-4724-910f-491c071c7664 · outbound

This paper cites Fjord: Fair and accurate federated learning under heteroge- neous targets with ordered dropout,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Fjord: Fair and accurate federated learning under heteroge- neous targets with ordered dropout,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:53:59.969942Z

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Observation e99de1e5-a544-4094-ad35-fa9c98102b90 · outbound

This paper cites Feddse: Distribution-aware sub-model extraction for federated learning over resource-constrained devices,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Feddse: Distribution-aware sub-model extraction for federated learning over resource-constrained devices,

Reference 35

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Observation 083c8ea5-577e-47d1-9ca1-2ba9750b6765 · outbound

This paper cites Fedrolex: Model-heterogeneous federated learning with rolling sub-model extraction,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Fedrolex: Model-heterogeneous federated learning with rolling sub-model extraction,

Reference 36

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Observation b4f3f879-e1bb-41d8-929a-ec096c1dfbee · outbound

This paper cites Efficient split-mix federated learning for on-demand and in-situ customization,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Efficient split-mix federated learning for on-demand and in-situ customization,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:53:59.917550Z

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Observation 2f21efba-c8f8-43c9-ba44-1cac25d8448a · outbound

This paper cites FedMD: Heterogenous Federated Learning via Model Distillation.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices FedMD: Heterogenous Federated Learning via Model Distillation

Reference 38

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Observation c840ee1e-39bc-4785-a33e-a7aba1961e1d · outbound

This paper cites Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:53:59.901873Z

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Observation 0eb513b3-8fb9-4c5e-9438-cff1ab6fd350 · outbound

This paper cites Group knowledge transfer: Federated learning of large cnns at the edge,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Group knowledge transfer: Federated learning of large cnns at the edge,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:53:59.885286Z

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Observation 426267b7-3bbd-42ab-a4a9-70fef38664fd · outbound

This paper cites Exploring the distributed knowledge congruence in proxy-data-free federated distillation,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Exploring the distributed knowledge congruence in proxy-data-free federated distillation,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:53:59.868896Z

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Observation d3e63931-52ea-4a6d-ba83-edb576bcb3d3 · outbound

This paper cites Distributed learning of deep neural network over multiple agents,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Distributed learning of deep neural network over multiple agents,

Reference 42

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Observation d4bb6c1e-3620-4245-aa20-a124df4fc3ed · outbound

This paper cites Federated mutual learning: a collaborative machine learning method for heterogeneous data, models, and objectives,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Federated mutual learning: a collaborative machine learning method for heterogeneous data, models, and objectives,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:53:59.841819Z

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Observation 74cb0458-3bd1-424f-9084-aab79dd795b5 · outbound

This paper cites Pervasivefl: Pervasive federated learning for heterogeneous iot systems,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Pervasivefl: Pervasive federated learning for heterogeneous iot systems,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:53:59.825859Z

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Observation 3078be59-6c75-4ae1-b5ad-aff34136ca9e · outbound

This paper cites Deep mutual learning,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Deep mutual learning,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:53:59.809712Z

Source-reported events for the cited work

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Observation 78fe00c1-dd76-42ef-96b3-6aa05d39a22b · outbound

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

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Ensemble distillation for robust model fusion in federated learning,

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-08T04:53:59.065843Z

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Observation ab3b11e0-9102-4465-acca-44884120f695 · outbound

This paper cites Distillation-based semi-supervised federated learning for communication-efficient collaborative training with non-iid private data,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Distillation-based semi-supervised federated learning for communication-efficient collaborative training with non-iid private data,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:53:59.783468Z

Source-reported events for the cited work

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Observation 643baad4-2daf-40af-856f-bfcbb0aa4705 · outbound

This paper cites Heteroge- neous ensemble knowledge transfer for training large models in federated learning,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Heteroge- neous ensemble knowledge transfer for training large models in federated learning,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:53:59.766174Z

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Observation cb6bc30d-20c6-44c6-99bb-8dce8e3b13c8 · outbound

This paper cites Data-free learning of student networks,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Data-free learning of student networks,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:53:59.748941Z

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Observation 0407acfb-52f1-49d2-b698-9ec0fe6a6e0e · outbound

This paper cites Momentum adversarial distillation: Handling large distribution shifts in data-free knowledge distillation,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Momentum adversarial distillation: Handling large distribution shifts in data-free knowledge distillation,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:53:59.732747Z

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Observation 61910e71-4947-4e84-91d7-4d50d726d795 · outbound

This paper cites Fine-tuning global model via data-free knowledge distillation for non-iid federated learning,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Fine-tuning global model via data-free knowledge distillation for non-iid federated learning,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:53:59.713613Z

Source-reported events for the cited work

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Observation 4797e08b-c9ea-40cd-8829-0fcbdaedceeb · outbound

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

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Dfrd: Data-free robustness distillation for heterogeneous federated learning,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:53:59.697647Z

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Observation 02498923-dfe8-4ba8-bb1a-ffdb0bb2015d · outbound

This paper cites One-shot fed- erated learning: theoretical limits and algorithms to achieve them,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices One-shot fed- erated learning: theoretical limits and algorithms to achieve them,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:53:59.681250Z

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Observation 4500e7ae-38ba-44bd-a1f5-c63747e5738b · outbound

This paper cites Distilling knowledge via knowledge review,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Distilling knowledge via knowledge review,

Reference 54

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Observation d88dffee-1bfd-447b-b193-2078a0cb29fd · outbound

This paper cites Auto-Encoding Variational Bayes.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Auto-Encoding Variational Bayes

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-08T04:53:59.107620Z

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Observation d291ec45-12a6-40dd-8706-d0cc6a2a2aab · outbound

This paper cites On information and sufficiency,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices On information and sufficiency,

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-08T04:53:59.112278Z

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Observation 10f1aedf-6180-4d9e-850a-8206ef834778 · outbound

This paper cites Least squares quantization in pcm,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Least squares quantization in pcm,

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-08T04:53:59.117030Z

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Observation 3e3a2428-0e22-4f26-b6c6-afdad32ac524 · outbound

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

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Gradient-based learning applied to document recognition,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:53:59.634106Z

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Observation 49bc52b1-49f1-4ce9-afde-14b35351625c · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 59

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Observation aec550ac-24ae-4bff-b5d3-0bbc136a7de2 · outbound

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

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Reading digits in natural images with unsupervised feature learning,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:53:59.617791Z

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Observation 5c6b102e-4270-4c8c-a669-a396b5ef85b1 · outbound

This paper cites Emnist: Extending mnist to handwritten letters,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Emnist: Extending mnist to handwritten letters,

Reference 61

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Observation 7a4441fa-2b5e-4978-a94f-3071593b6efb · outbound

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

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification

Reference 62

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Observation e841330e-32fe-4647-914a-d4a7cb3570c4 · outbound

This paper cites Federated optimization in heterogeneous networks,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Federated optimization in heterogeneous networks,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:53:59.590036Z

Source-reported events for the cited work

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Observation aa0f2b17-533f-4684-869b-b6c4b50a13dd · outbound

This paper cites Scaffold: Stochastic controlled averaging for federated learn- ing,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Scaffold: Stochastic controlled averaging for federated learn- ing,

Reference 64

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Observation 40e652d6-37a8-41ce-b39c-85d9e5571744 · outbound

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

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Data-free knowledge distillation for het- erogeneous federated learning,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:53:59.563491Z

Source-reported events for the cited work

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Observation 50f1b9b5-8eb0-4ee7-93b8-f579b341cfd7 · outbound

This paper cites Efficientnetv2: Smaller models and faster training,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Efficientnetv2: Smaller models and faster training,

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-08T04:53:59.160041Z

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Observation e6d2bf9d-320c-4d76-a272-d34e2b000625 · outbound

This paper cites Deep residual learning for image recognition,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Deep residual learning for image recognition,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:53:59.536807Z

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 fc95cab8-b432-438d-a4b9-67d8e870ed37 · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Mobilenetv2: Inverted residuals and linear bottlenecks,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:53:59.520057Z

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 6d982b97-b372-4bb1-a5b8-8cc9d0adc42b · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium,.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices Gans trained by a two time-scale update rule converge to a local nash equilibrium,

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-08T04:53:59.174802Z

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