An Overview of Data Contribution Evaluation Methods for Federated Learning

With the rapid development of artificial intelligence, especially machine learning technology, the demand for data is also increasing. As a distributed learning method, federated learning (FL) allows total multiple parties to co-train models locally while protecting data privacy. Due to heterogeneity in data, devices, and other factors, the contribution of participants to model quality also varies. If these differences are ignored, it poses a challenge to achieving a fair FL environment. Therefore, fairly assessing the contribution levels of each data provider becomes a critical issue in FL. This plays a significant role in fairly motivating participants and promoting the sustainable development of FL. This paper reviews existing evaluation methods from the perspective of data contribution assessment in FL, discusses ongoing challenges in this area, and explores future research directions.