Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection | Paper Citation Record

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This paper cites Badr, Mohamed M.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Badr, Mohamed M

Reference 1

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Observation 10eae771-bd7d-40f0-91c2-f9f10e5c5653 · outbound

This paper cites Diverse client selection for federated learning: Submodularity and convergence anal- ysis.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Diverse client selection for federated learning: Submodularity and convergence anal- ysis

Reference 2

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Observation db83b00f-e949-4cb8-b90d-2fa15aede043 · outbound

This paper cites Towards Federated Learning at Scale: System Design.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Towards Federated Learning at Scale: System Design

Reference 3

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This paper cites Evaluating feder- ated learning for intrusion detection in internet of things: Review and challenges.Computer Networks, 203:108661, 2022.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Evaluating feder- ated learning for intrusion detection in internet of things: Review and challenges.Computer Networks, 203:108661, 2022

Reference 4

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This paper cites A credible and fair federated learning framework based on blockchain.IEEE Transactions on Artificial Intelligence, 6(2):301–316, February 2025.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection A credible and fair federated learning framework based on blockchain.IEEE Transactions on Artificial Intelligence, 6(2):301–316, February 2025

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:27.630809Z

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This paper cites Emnist: Extend- ing mnist to handwritten letters.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Emnist: Extend- ing mnist to handwritten letters

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:27.377170Z

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Observation 38b04737-f04d-438f-a56b-b4cd3291d113 · outbound

This paper cites Local model poisoning attacks to byzantine-robust federated learning.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Local model poisoning attacks to byzantine-robust federated learning

Reference 7

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This paper cites Clustered sampling: Low- variance and improved representativity for clients selection in federated learning.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Clustered sampling: Low- variance and improved representativity for clients selection in federated learning

Reference 8

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Observation 1a96e181-efb6-4fe6-bd70-2e294a6d67dc · outbound

This paper cites Data shapley: Equitable valuation of data for machine learn- ing.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Data shapley: Equitable valuation of data for machine learn- ing

Reference 9

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Observation f02c36b6-3af3-4176-b093-e5d1a80828a8 · outbound

This paper cites Victoria Luzón.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Victoria Luzón

Reference 10

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This paper cites Promoting collaboration in cross-silo federated learning: Challenges and opportunities.IEEE Communications Magazine, 62(4):82–88, April 2024.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Promoting collaboration in cross-silo federated learning: Challenges and opportunities.IEEE Communications Magazine, 62(4):82–88, April 2024

Reference 11

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Observation 0f7f734d-1d22-412a-b6fa-e8de1c489ed3 · outbound

This paper cites Towards understanding biased client selection in federated learning.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Towards understanding biased client selection in federated learning

Reference 12

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Observation a6e1728a-2818-4aa8-97dc-9abd2865550c · outbound

This paper cites Optimal user selection for high-performance and stabilized energy-efficient feder- ated learning platforms.Electronics, 9(9):1359, 2020.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Optimal user selection for high-performance and stabilized energy-efficient feder- ated learning platforms.Electronics, 9(9):1359, 2020

Reference 13

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This paper cites Toward an automated auction framework for wireless federated learning services market.IEEE Transactions on Mobile Computing, 20(10):3034–3048, 2020.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Toward an automated auction framework for wireless federated learning services market.IEEE Transactions on Mobile Computing, 20(10):3034–3048, 2020

Reference 14

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This paper cites Prospect theory: An analysis of decision under risks.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Prospect theory: An analysis of decision under risks

Reference 15

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This paper cites an unresolved cited work.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Unresolved cited work

Reference 16

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Observation 0e1be66d-216f-4cbf-82ee-cdeea3ae9573 · outbound

This paper cites Khan, Shashi Raj Pandey, Nguyen H.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Khan, Shashi Raj Pandey, Nguyen H

Reference 17

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Observation bad19ed7-3af5-47c0-8bd8-0262e80dacd9 · outbound

This paper cites Federated Optimization: Distributed Machine Learning for On-Device Intelligence.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Federated Optimization: Distributed Machine Learning for On-Device Intelligence

Reference 18

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

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Learning multiple layers of features from tiny images

Reference 19

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This paper cites Data distribution-aware online client selection algorithm for federated learning in heterogeneous networks.IEEE Transac- tions on Vehicular Technology, 72(1):1127–1136, 2023.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Data distribution-aware online client selection algorithm for federated learning in heterogeneous networks.IEEE Transac- tions on Vehicular Technology, 72(1):1127–1136, 2023

Reference 20

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This paper cites A review of applications in federated learning.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection A review of applications in federated learning

Reference 21

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

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection On the conver- gence of fedavg on non-iid data

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:24.569784Z

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This paper cites Gtg-shapley: Efficient and accurate participant contribution evaluation in federated learning.ACM Transactions on intel- ligent Systems and Technology (TIST), 13(4):1–21, 2022.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Gtg-shapley: Efficient and accurate participant contribution evaluation in federated learning.ACM Transactions on intel- ligent Systems and Technology (TIST), 13(4):1–21, 2022

Reference 23

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Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Unresolved cited work

Reference 24

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

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Communication-efficient learning of deep networks from decentralized data

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:24.061873Z

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This paper cites an unresolved cited work.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Unresolved cited work

Reference 26

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This paper cites Client selection for federated learning with heterogeneous resources in mobile edge.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Client selection for federated learning with heterogeneous resources in mobile edge

Reference 27

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This paper cites An incentive auction for heteroge- neous client selection in federated learning.IEEE Transactions on Mobile Computing, 22(10): 5733–5750, 2022.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection An incentive auction for heteroge- neous client selection in federated learning.IEEE Transactions on Mobile Computing, 22(10): 5733–5750, 2022

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This paper cites Feddcs: A distributed client selection framework for cross device federated learning.Future Generation Computer Systems, 144: 24–36, July 2023.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Feddcs: A distributed client selection framework for cross device federated learning.Future Generation Computer Systems, 144: 24–36, July 2023

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This paper cites High- quality model aggregation for blockchain-based federated learning via reputation-motivated task participation.IEEE Internet of Things Journal, 9(19):18378–18391, 2022.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection High- quality model aggregation for blockchain-based federated learning via reputation-motivated task participation.IEEE Internet of Things Journal, 9(19):18378–18391, 2022

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This paper cites Federated learning-based ai approaches in smart healthcare: concepts, taxonomies, challenges and open issues.Cluster computing, 26(4):2271–2311, 2023.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Federated learning-based ai approaches in smart healthcare: concepts, taxonomies, challenges and open issues.Cluster computing, 26(4):2271–2311, 2023

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:22.626605Z

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This paper cites Survey on federated learning threats: Concepts, taxonomy on attacks and defences, experimental study and challenges.Information Fusion, 90:148–173, 2023.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Survey on federated learning threats: Concepts, taxonomy on attacks and defences, experimental study and challenges.Information Fusion, 90:148–173, 2023

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:22.405494Z

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This paper cites Motivating workers in federated learning: A stackelberg game perspective.IEEE Networking Letters, 2(1):23–27, 2020.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Motivating workers in federated learning: A stackelberg game perspective.IEEE Networking Letters, 2(1):23–27, 2020

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:22.252292Z

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Observation aaa898f0-fdbe-4fd1-a2c4-482defc52466 · outbound

This paper cites an unresolved cited work.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Unresolved cited work

Reference 34

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This paper cites Yang, Xijun Wang, Yan Zhang, and Tony Q.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Yang, Xijun Wang, Yan Zhang, and Tony Q

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:21.831608Z

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This paper cites Data poisoning attacks on federated machine learning.IEEE Internet of Things Journal, 9(13):11365–11375, 2022.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Data poisoning attacks on federated machine learning.IEEE Internet of Things Journal, 9(13):11365–11375, 2022

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:21.608013Z

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This paper cites Incentive mechanism design for joint resource allocation in blockchain-based federated learning.IEEE Transactions on Parallel and Distributed Systems, 34(5):1536–1547, May 2023.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Incentive mechanism design for joint resource allocation in blockchain-based federated learning.IEEE Transactions on Parallel and Distributed Systems, 34(5):1536–1547, May 2023

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:21.452614Z

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This paper cites A survey on federated learning: challenges and applications.International Journal of Machine Learning and Cybernetics, 14(2):513–535, 2023.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection A survey on federated learning: challenges and applications.International Journal of Machine Learning and Cybernetics, 14(2):513–535, 2023

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:21.270933Z

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

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

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This paper cites Jointly optimizing client selection and resource management in wireless federated learning for internet of things.IEEE Internet of Things Journal, 9(6):4385–4395, 2021.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Jointly optimizing client selection and resource management in wireless federated learning for internet of things.IEEE Internet of Things Journal, 9(6):4385–4395, 2021

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:21.068555Z

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Observation 9ef58fb7-b4f5-453a-8665-85d14acc2b28 · outbound

This paper cites Yu, and Christopher G.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Yu, and Christopher G

Reference 41

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This paper cites A survey of trustworthy federated learning: Issues, solutions, and challenges.ACM Transactions on Intelligent Systems and Technology, 15(6):1–47, October 2024.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection A survey of trustworthy federated learning: Issues, solutions, and challenges.ACM Transactions on Intelligent Systems and Technology, 15(6):1–47, October 2024

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:20.699715Z

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Observation dc3a73cb-16c1-46fe-a8d1-67cfb5949a28 · outbound

This paper cites an unresolved cited work.

Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection Unresolved cited work

Reference 43

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