mlsanityData to 7 Oct 2026

FedCCEA : A Practical Approach of Client Contribution Evaluation for Federated Learning

arXiv 2106.02310 ↗First posted 4 Jun 2021cs.LG cs.AI cs.DCcited by 14

Not on the map. Why?

Abstract (arXiv)

Client contribution evaluation, also known as data valuation, is a crucial approach in federated learning(FL) for client selection and incentive allocation. However, due to restrictions of accessibility of raw data, only limited information such as local weights and local data size of each client is open for quantifying the client contribution. Using data size from available information, we introduce an empirical evaluation method called Federated Client Contribution Evaluation through Accuracy Approximation(FedCCEA). This method builds the Accuracy Approximation Model(AAM), which estimates a simulated test accuracy using inputs of sampled data size and extracts the clients' data quality and data size to measure client contribution. FedCCEA strengthens some advantages: (1) enablement of data size selection to the clients, (2) feasible evaluation time regardless of the number of clients, and (3) precise estimation in non-IID settings. We demonstrate the superiority of FedCCEA compared to previous methods through several experiments: client contribution distribution, client removal, and robustness test to partial participation.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 23 of its 41 references that are in the map. First posted on arXiv 4 Jun 2021.

Builds on

23 of 41 references.

References · 23

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2017Understanding Black-box Predictions via Influence Functionscited by 1,714
  5. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  6. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  7. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  8. 2018An Empirical Study of Example Forgetting during Deep Neural Network Learningcited by 669
  9. 2019Data Shapley: Equitable Valuation of Data for Machine Learningcited by 617
  10. 2020Federated Learning with Matched Averagingcited by 564
  11. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  12. 2018Analyzing Federated Learning through an Adversarial Lenscited by 346
11 more
  1. 2019Towards Efficient Data Valuation Based on the Shapley Valuecited by 279
  2. 2020A Principled Approach to Data Valuation for Federated Learningcited by 127
  3. 2019Data Valuation using Reinforcement Learningcited by 115
  4. 2020Collaborative Fairness in Federated Learningcited by 95
  5. 2020Learning to Detect Malicious Clients for Robust Federated Learningcited by 86
  6. 2019Measure Contribution of Participants in Federated Learningcited by 78
  7. 2013Bounding the Estimation Error of Sampling-based Shapley Value Approximationcited by 56
  8. 2018No Peek: A Survey of private distributed deep learningcited by 30
  9. 2019Rewarding High-Quality Data via Influence Functionscited by 11
  10. 2020Overcoming Noisy and Irrelevant Data in Federated Learningcited by 6
  11. 2016Market Model and Optimal Pricing Scheme of Big Data and Internet of Things (IoT)cited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 3; 2025: 6; 2026: 1.

14 papers.

Sort:

By area:

  1. 2021Towards Fairness-Aware Federated Learningcited by 57
  2. 2025Ten Challenging Problems in Federated Foundation Modelscited by 20
  3. 2025Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networkscited by 6
  4. 2023Exploring Data Redundancy in Real-world Image Classification through Data Selectioncited by 3
  5. 2023Fairness and Privacy in Federated Learning and Their Implications in Healthcarecited by 3
  6. 2024Contribution Evaluation of Heterogeneous Participants in Federated Learning via Prototypical Representationscited by 3
  7. 2024Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learningcited by 1
  8. 2025MTF-Grasp: A Multi-tier Federated Learning Approach for Robotic Graspingcited by 1
  9. 2022Mitigating Data Absence in Federated Learning Using Privacy-Controllable Data Digestscited by 0
  10. 2024Towards Robust Federated Analytics via Differentially Private Measurements of Statistical Heterogeneitycited by 0
  11. 2025Overcoming Challenges of Partial Client Participation in Federated Learning : A Comprehensive Reviewcited by 0
  12. 2025Owen Sampling Accelerates Contribution Estimation in Federated Learningcited by 0
2 more
  1. 2025PQFed: A Privacy-Preserving Quality-Controlled Federated Learning Frameworkcited by 0
  2. 2026Sample Selection Using Multi-Task Autoencoders in Federated Learning with Non-IID Datacited by 0