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

arXiv: https://arxiv.org/abs/2106.02310 · HTML: https://mlsanity.com/paper/2106.02310/

## Key facts

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

## Builds on (most cited first, 23 of 23)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [An Empirical Study of Example Forgetting during Deep Neural Network Learning](https://mlsanity.com/paper/1812.05159/) (2018)
- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://mlsanity.com/paper/1904.02868/) (2019)
- [Federated Learning with Matched Averaging](https://mlsanity.com/paper/2002.06440/) (2020)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [Analyzing Federated Learning through an Adversarial Lens](https://mlsanity.com/paper/1811.12470/) (2018)
- [Towards Efficient Data Valuation Based on the Shapley Value](https://mlsanity.com/paper/1902.10275/) (2019)
- [A Principled Approach to Data Valuation for Federated Learning](https://mlsanity.com/paper/2009.06192/) (2020)
- [Data Valuation using Reinforcement Learning](https://mlsanity.com/paper/1909.11671/) (2019)
- [Collaborative Fairness in Federated Learning](https://mlsanity.com/paper/2008.12161/) (2020)
- [Learning to Detect Malicious Clients for Robust Federated Learning](https://mlsanity.com/paper/2002.00211/) (2020)
- [Measure Contribution of Participants in Federated Learning](https://mlsanity.com/paper/1909.08525/) (2019)
- [Bounding the Estimation Error of Sampling-based Shapley Value Approximation](https://mlsanity.com/paper/1306.4265/) (2013)
- [No Peek: A Survey of private distributed deep learning](https://mlsanity.com/paper/1812.03288/) (2018)
- [Rewarding High-Quality Data via Influence Functions](https://mlsanity.com/paper/1908.11598/) (2019)
- [Overcoming Noisy and Irrelevant Data in Federated Learning](https://mlsanity.com/paper/2001.08300/) (2020)
- [Market Model and Optimal Pricing Scheme of Big Data and Internet of Things (IoT)](https://mlsanity.com/paper/1602.03202/) (2016)

## Built on by (most cited first, 14 of 14)

- [Towards Fairness-Aware Federated Learning](https://mlsanity.com/paper/2111.01872/) (2021)
- [Ten Challenging Problems in Federated Foundation Models](https://mlsanity.com/paper/2502.12176/) (2025)
- [Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks](https://mlsanity.com/paper/2502.04850/) (2025)
- [Exploring Data Redundancy in Real-world Image Classification through Data Selection](https://mlsanity.com/paper/2306.14113/) (2023)
- [Fairness and Privacy in Federated Learning and Their Implications in Healthcare](https://mlsanity.com/paper/2308.07805/) (2023)
- [Contribution Evaluation of Heterogeneous Participants in Federated Learning via Prototypical Representations](https://mlsanity.com/paper/2407.02073/) (2024)
- [Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learning](https://mlsanity.com/paper/2405.13584/) (2024)
- [MTF-Grasp: A Multi-tier Federated Learning Approach for Robotic Grasping](https://mlsanity.com/paper/2507.10158/) (2025)
- [Mitigating Data Absence in Federated Learning Using Privacy-Controllable Data Digests](https://mlsanity.com/paper/2210.00737/) (2022)
- [Towards Robust Federated Analytics via Differentially Private Measurements of Statistical Heterogeneity](https://mlsanity.com/paper/2411.04579/) (2024)
- [Overcoming Challenges of Partial Client Participation in Federated Learning : A Comprehensive Review](https://mlsanity.com/paper/2506.02887/) (2025)
- [Owen Sampling Accelerates Contribution Estimation in Federated Learning](https://mlsanity.com/paper/2508.21261/) (2025)
- [PQFed: A Privacy-Preserving Quality-Controlled Federated Learning Framework](https://mlsanity.com/paper/2509.21704/) (2025)
- [Sample Selection Using Multi-Task Autoencoders in Federated Learning with Non-IID Data](https://mlsanity.com/paper/2604.26116/) (2026)

## 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.