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Federated Learning (FL) provides a privacy-preserving and decentralized approach to collaborative machine learning for multiple FL clients. The contribution estimation mechanism in FL is extensively studied within the database community, which aims to compute fair and reasonable contribution scores as incentives to motivate FL clients. However, designing such methods involves challenges in three aspects: effectiveness, robustness, and efficiency. Firstly, contribution estimation methods should utilize the data utility information of various client coalitions rather than that of individual clients to ensure effectiveness. Secondly, we should beware of adverse clients who may exploit tactics like data replication or label flipping. Thirdly, estimating contribution in FL can be time-consuming due to enumerating various client coalitions. Despite numerous proposed methods to address these challenges, each possesses distinct advantages and limitations based on specific settings. However, existing methods have yet to be thoroughly evaluated and compared in the same experimental framework. Therefore, a unified and comprehensive evaluation framework is necessary to compare these methods under the same experimental settings. This paper conducts an extensive survey of contribution estimation methods in FL and introduces a comprehensive framework to evaluate their effectiveness, robustness, and efficiency. Through empirical results, we present extensive observations, valuable discoveries, and an adaptable testing framework that can facilitate future research in designing and evaluating contribution estimation methods in FL.
Chen et al. (Mon,) studied this question.