Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout | Paper Citation Record

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This paper cites Brendan McMahan, Brendan Avent, Aurélien Bellet, and Mehdi Bennis et al.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Brendan McMahan, Brendan Avent, Aurélien Bellet, and Mehdi Bennis et al

Reference 1

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Observation 3edd3e68-972c-4ea8-a1bc-25f721a1b5a8 · outbound

This paper cites Trustworthy federated learning: Privacy, security, and beyond.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Trustworthy federated learning: Privacy, security, and beyond

Reference 2

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This paper cites Enhancing trust and privacy in distributed networks: a comprehensive survey on blockchain-based federated learning.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Enhancing trust and privacy in distributed networks: a comprehensive survey on blockchain-based federated learning

Reference 3

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This paper cites From distributed machine learning to federated learning: a survey.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout From distributed machine learning to federated learning: a survey

Reference 4

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This paper cites General data protection regulation.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout General data protection regulation

Reference 5

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This paper cites California consumer privacy act home page.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout California consumer privacy act home page

Reference 6

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Observation 1117acd5-3bda-4d28-8eb8-347aa99ba5d3 · outbound

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

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Communication-efficient learning of deep networks from decentralized data

Reference 7

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This paper cites Heterps: Distributed deep learning with reinforcement learning based scheduling in heterogeneous environments.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Heterps: Distributed deep learning with reinforcement learning based scheduling in heterogeneous environments

Reference 8

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This paper cites Scaling distributed machine learning with the parameter server.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Scaling distributed machine learning with the parameter server

Reference 9

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Observation 6c1c9c4f-b72a-4918-92bf-f27a7e21732f · outbound

This paper cites Vincent Poor.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Vincent Poor

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:06.311301Z

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Observation ee761d31-6baa-4e82-8848-e9d0b4b5a173 · outbound

This paper cites Multi-job intelligent scheduling with cross-device federated learning.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Multi-job intelligent scheduling with cross-device federated learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:06.295181Z

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Observation 0d6c6193-3b57-45ef-952e-4a058250b352 · outbound

This paper cites Efficient device scheduling with multi-job federated learning.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Efficient device scheduling with multi-job federated learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:06.279260Z

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Observation 0a37ecb4-b26a-4b86-b08d-3e34ce7a3292 · outbound

This paper cites Federated learning on non-iid data silos: An experimental study.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Federated learning on non-iid data silos: An experimental study

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:06.263615Z

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Observation 01ec500b-8a9a-44da-9adf-af2a5b772c13 · outbound

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

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout On the convergence of fedavg on non-iid data

Reference 14

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This paper cites Federated optimization in heterogeneous networks.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Federated optimization in heterogeneous networks

Reference 15

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This paper cites Jensen-shannon divergence and Hilbert space embedding.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Jensen-shannon divergence and Hilbert space embedding

Reference 16

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Observation 384c9bc1-092a-436e-922d-27ec90a27c3b · outbound

This paper cites Information theory and statistics.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Information theory and statistics

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:06.199499Z

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Observation 73f3bdb2-d817-44be-b6ef-b5feb279f3ba · outbound

This paper cites Multi-Center Federated Learning: Clients Clustering for Better Personalization.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Multi-Center Federated Learning: Clients Clustering for Better Personalization

Reference 18

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Observation 95236e4f-8f13-4da2-827c-292543d2776d · outbound

This paper cites Towards federated learning at scale: System design.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Towards federated learning at scale: System design

Reference 19

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Observation 8696325d-e313-4d24-8806-b764f8ba70bc · outbound

This paper cites Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:06.163362Z

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Observation 66602b4a-5385-40d3-9c50-acb289fc222e · outbound

This paper cites Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:06.144986Z

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Observation 929add3c-cd28-4298-9f7d-ff581e57cb3d · outbound

This paper cites Adaptive federated dropout: Improving communication efficiency and generalization for federated learning.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Adaptive federated dropout: Improving communication efficiency and generalization for federated learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:06.128344Z

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Observation 5e8a0054-a991-4dff-a0fa-2ea9210d159c · outbound

This paper cites Federated dropout–a simple approach for enabling federated learning on resource constrained devices.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Federated dropout–a simple approach for enabling federated learning on resource constrained devices

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:06.110117Z

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Observation be337498-8e05-41c2-a40e-5ca93212d6c4 · outbound

This paper cites Federated learning based on dynamic regularization.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Federated learning based on dynamic regularization

Reference 24

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Observation fe608f1d-3cc8-437e-935f-10860429bb8b · outbound

This paper cites Model-contrastive federated learning.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Model-contrastive federated learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:06.075683Z

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Observation 87c74c6e-979c-4375-afe1-bf3f0277a444 · outbound

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

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout SCAFFOLD: Stochastic controlled averaging for federated learning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:06.059509Z

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Observation 1afad173-ec60-49e9-9586-65f3448307d8 · outbound

This paper cites Partialfed: Cross-domain personalized federated learning via partial initialization.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Partialfed: Cross-domain personalized federated learning via partial initialization

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:06.039855Z

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This paper cites Sageflow: Robust federated learning against both stragglers and adversaries.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Sageflow: Robust federated learning against both stragglers and adversaries

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:06.020608Z

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This paper cites Efficient asynchronous federated learning with sparsification and quantization.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Efficient asynchronous federated learning with sparsification and quantization

Reference 29

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This paper cites Aedfl: efficient asyn- chronous decentralized federated learning with heterogeneous devices.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Aedfl: efficient asyn- chronous decentralized federated learning with heterogeneous devices

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:05.987033Z

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Observation 591e6c4b-e932-4999-9d3b-65cf3cea1d2e · outbound

This paper cites Efficient federated learning with timely update dissemination.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Efficient federated learning with timely update dissemination

Reference 31

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Observation 9d5e9706-8675-44e5-a72f-5a7c48fe7940 · outbound

This paper cites Fedasmu: Efficient asynchronous federated learning with dynamic staleness-aware model update.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Fedasmu: Efficient asynchronous federated learning with dynamic staleness-aware model update

Reference 32

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Observation 676af114-be98-447a-b9ed-4e4ea58cb025 · outbound

This paper cites Federated machine learning: Concept and applications.ACM Transactions on Intelligent Systems and Technology (TIST), 10(2):1–19, 2019.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Federated machine learning: Concept and applications.ACM Transactions on Intelligent Systems and Technology (TIST), 10(2):1–19, 2019

Reference 33

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This paper cites Exploring one-shot semi-supervised federated learning with pre-trained diffusion models.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Exploring one-shot semi-supervised federated learning with pre-trained diffusion models

Reference 34

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This paper cites Jamaloddin Golestani.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Jamaloddin Golestani

Reference 35

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This paper cites Semi-cyclic stochastic gradient descent.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Semi-cyclic stochastic gradient descent

Reference 36

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Observation 3d9b4e57-2527-4e94-a517-d7ab6c4d601d · outbound

This paper cites Benchmarking FedAvg and FedCurv for Image Classification Tasks.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Benchmarking FedAvg and FedCurv for Image Classification Tasks

Reference 37

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This paper cites Fair federated learning under domain skew with local consistency and domain diversity.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Fair federated learning under domain skew with local consistency and domain diversity

Reference 38

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This paper cites Accelerated federated learning with decoupled adaptive optimization.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Accelerated federated learning with decoupled adaptive optimization

Reference 39

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This paper cites Adaptive gradient-based meta-learning methods.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Adaptive gradient-based meta-learning methods

Reference 40

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This paper cites Federated multi-task learning.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Federated multi-task learning

Reference 41

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Observation 90036c44-590a-45e8-b891-90a6e74d81ca · outbound

This paper cites Adversarial collaborative learning on non-iid features.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Adversarial collaborative learning on non-iid features

Reference 42

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This paper cites Generalizable heterogeneous federated cross-correlation and instance similarity learning.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Generalizable heterogeneous federated cross-correlation and instance similarity learning

Reference 43

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Observation d5b01b30-fd4b-4658-a864-0f1fd7b68222 · outbound

This paper cites Bayesian nonparametric federated learning of neural networks.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Bayesian nonparametric federated learning of neural networks

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:05.769323Z

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Observation 637bdf00-758c-4350-a5e9-d9125979b79a · outbound

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

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Ensemble distillation for robust model fusion in federated learning

Reference 45

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Observation 110d5c90-545d-4281-9312-ae824f94aac5 · outbound

This paper cites An upload-efficient scheme for transferring knowledge from a server-side pre-trained generator to clients in heterogeneous federated learning.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout An upload-efficient scheme for transferring knowledge from a server-side pre-trained generator to clients in heterogeneous federated learning

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:05.731874Z

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Observation 9c991f12-511e-4f3c-8331-ec386250cbcf · outbound

This paper cites AugFL: Augmenting Federated Learning with Pretrained Models.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout AugFL: Augmenting Federated Learning with Pretrained Models

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:38:05.206865Z

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Observation cde9f457-f3c6-4b9f-b01c-c983ed71890a · outbound

This paper cites Grounding Foundation Models through Federated Transfer Learning: A General Framework.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Grounding Foundation Models through Federated Transfer Learning: A General Framework

Reference 48

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Observation 8c4364d0-2a31-47ad-83c6-3de55cecb7fc · outbound

This paper cites Big-fed: Bilevel optimization enhanced graph-aided federated learning.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Big-fed: Bilevel optimization enhanced graph-aided federated learning

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:05.714062Z

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Observation 19246e48-24d3-4001-83fc-bfed8d9fbe28 · outbound

This paper cites Model pruning enables efficient federated learning on edge devices.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Model pruning enables efficient federated learning on edge devices

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:05.697233Z

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Observation 5ca28982-1148-4cdc-b83e-cd9552eab329 · outbound

This paper cites Federated dynamic sparse training: Computing less, communicating less, yet learning better.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Federated dynamic sparse training: Computing less, communicating less, yet learning better

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:05.679291Z

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Observation 35df1fb6-5f85-4c60-8065-e11b0ca17cdc · outbound

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

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Federated learning from pre-trained models: A contrastive learning approach

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:05.662139Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 69dd4713-4adc-4ed1-9ad7-33b58212859f · outbound

This paper cites Oort: Efficient federated learning via guided participant selection.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Oort: Efficient federated learning via guided participant selection

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:05.639501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7735abfd-7eb4-48ca-b97a-6fe201786576 · outbound

This paper cites Astraea: Self-balancing federated learning for improving classification accuracy of mobile deep learning applications.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Astraea: Self-balancing federated learning for improving classification accuracy of mobile deep learning applications

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:05.622374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d0afdff0-5e1f-4cff-9702-96a48a3d2424 · outbound

This paper cites Client selection for federated learning with label noise.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Client selection for federated learning with label noise

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:05.605897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 61950c60-792d-4deb-b942-dd9a33b673b2 · outbound

This paper cites FedDUAP: Federated learning with dynamic update and adaptive pruning using shared data on the server.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout FedDUAP: Federated learning with dynamic update and adaptive pruning using shared data on the server

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:05.589182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b0e2d63a-95f8-4f82-9324-4bda7dd1f64a · outbound

This paper cites Efficient federated learning using dynamic update and adaptive pruning with momentum on shared server data.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Efficient federated learning using dynamic update and adaptive pruning with momentum on shared server data

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:05.570011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6f0f59b9-94c9-4ad2-9e40-8f711926797f · outbound

This paper cites An improved federated learning algorithm for privacy-preserving in cybertwin-driven 6G system.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout An improved federated learning algorithm for privacy-preserving in cybertwin-driven 6G system

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:05.549677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4fc4fa06-8882-48ca-919f-2c0efe23bc1e · outbound

This paper cites Parallelized stochastic gradient descent.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Parallelized stochastic gradient descent

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:05.527819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b5b2cd3c-15a5-4aad-809f-b488b9017089 · outbound

This paper cites Approximation Methods for Bilevel Programming.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Approximation Methods for Bilevel Programming

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T17:38:05.009695Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39d625f4-4756-4251-90ca-851abc9cd392 · outbound

This paper cites A Two-Timescale Framework for Bilevel Optimization: Complexity Analysis and Application to Actor-Critic.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout A Two-Timescale Framework for Bilevel Optimization: Complexity Analysis and Application to Actor-Critic

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T17:38:05.015545Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d581df08-2f7b-48fa-aed7-ab49f8e802b1 · outbound

This paper cites Hrank: Filter pruning using high-rank feature map.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Hrank: Filter pruning using high-rank feature map

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:05.511131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3ff0fb95-2f16-4a8d-8c82-8ac699426d2b · outbound

This paper cites Seizing critical learning periods in federated learning.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Seizing critical learning periods in federated learning

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:05.493744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6e0c449e-29f1-4942-b405-c602e67bb718 · outbound

This paper cites Validating the lottery ticket hypothesis with inertial manifold theory.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Validating the lottery ticket hypothesis with inertial manifold theory

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:05.475404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 41a28c3c-daee-4d83-96d5-9774c3ed2949 · outbound

This paper cites Fedas: Bridging inconsistency in personalized federated learning.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Fedas: Bridging inconsistency in personalized federated learning

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:05.456566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 66d9877b-dd4f-42ce-bfcf-50f5718f1e57 · outbound

This paper cites Learning multiple layers of features from tiny images, 2009.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Learning multiple layers of features from tiny images, 2009

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-06T17:38:05.043627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e4be60d-922d-4db7-90d6-a916978fdc25 · outbound

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

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Reading digits in natural images with unsupervised feature learning

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:05.428137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0188b00a-2738-4fb3-9a03-ec2b02c845d9 · outbound

This paper cites Tiny imagenet visual recognition challenge.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Tiny imagenet visual recognition challenge

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:05.412123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f8564d1a-8f35-4dcd-a971-d675a8fce607 · outbound

This paper cites Handwritten digit recognition with a back-propagation network.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Handwritten digit recognition with a back-propagation network

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:05.394696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 44fc9697-237e-4340-8e5b-126fb8df68c3 · outbound

This paper cites Very deep convolutional networks for large-scale image recognition.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Very deep convolutional networks for large-scale image recognition

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:05.377237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a084d7a5-eb08-4d8f-91a7-8d24b1e5b2f4 · outbound

This paper cites Deep residual learning for image recognition.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Deep residual learning for image recognition

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:05.358026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cd5c797c-799e-4dfd-80e5-4d441e583f3e · outbound

This paper cites Fisher information- based efficient curriculum federated learning with large language models.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Fisher information- based efficient curriculum federated learning with large language models

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:05.340371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 67114d49-8a2e-461c-987a-d07b1a5eea14 · outbound

This paper cites Federated learning of large language models with parameter-efficient prompt tuning and adaptive optimization.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Federated learning of large language models with parameter-efficient prompt tuning and adaptive optimization

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:05.321602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 84b2c3c0-0de2-4416-b505-d58ba09cd323 · outbound

This paper cites Optimal distributed online prediction using mini-batches.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout Optimal distributed online prediction using mini-batches

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:05.302243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1f30ef1c-a787-42e9-9077-0c58d009c0e5 · outbound

This paper cites On the importance of the pearson correlation coefficient in noise reduction.

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout On the importance of the pearson correlation coefficient in noise reduction

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:38:05.278532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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