Federated learning is a collaborative machine learning framework with multiple participants whose training datasets are kept locally. How to evaluate the corresponding data contribution of each participant is one of the critical problems of federated learning. However, contribution evaluation in federated learning faces multiple challenges. First, to evaluate participant contribution, data value needs to be quantified, however, data valuation is challenging because it is subjective, task context-dependent, and vulnerable to malicious data. Second, participant contribution evaluation is a classic cooperative game problem, and a fair yet rational cooperative contribution evaluation scheme is needed to achieve an optimal equilibrium among all participants. Third, contribution evaluation schemes often involve exponential computational complexity, where data valuation by training models in federated learning is also quite time consuming. In recent years, researchers have conducted extensive studies on participant contribution evaluation in federated learning to tackle the above challenges. This study first introduces the background knowledge of federated learning and contribution evaluation. Then, data valuation metrics, contribution evaluation schemes, and corresponding optimization technologies are surveyed successively. Finally, the remaining challenges of contribution evaluation and potential future work are discussed.