Client Contribution

Client contribution, in various scientific contexts, focuses on quantifying the impact of individual components—be it data points, agents, or features—on a system's overall performance or output. Current research emphasizes developing methods to accurately and efficiently assess these contributions, often employing game-theoretic approaches like Shapley values, information theory, or influence functions, and incorporating them into model architectures such as deep neural networks and graph neural networks. This research is crucial for improving model interpretability, fairness, and robustness across diverse fields, ranging from machine learning and federated learning to natural language processing and biomedical applications.