With the rapid growth of large-scale datasets and complex AI models, federated learning has emerged as a promising approach to preserving data privacy. However, due to client heterogeneity and communication constraints, client selection serves as a crucial optimization problem. In real-world scenarios, varying data volume and dynamic client training states create a volatile environment. These factors lead to training delays and communication interruptions, significantly reducing the efficiency of federated learning. To address these challenges, a volatile-aware client selection method named CDE3 is proposed. First, we establish a multi-dimensional model to evaluate each client’s contribution. Then, the Exp3 algorithm is enhanced by incorporating a discount factor that exponentially weights historical contributions, and this refined algorithm assigns selection probabilities to clients based on their discounted historical contributions, enabling the server to make informed client selections. Finally, experimental results demonstrate that CDE3 effectively combats the volatility of the environment while maintaining high efficiency.