Recent advances in decentralized systems and the widespread adoption of smart devices have enabled a new era of distributed machine learning, offering real-time data processing and personalized services. However, this paradigm brings significant challenges in preserving data privacy, particularly as regulations emphasize the “right to be forgotten.” In this paper, we present a novel and efficient federated unlearning method that successfully removes the influence of forgotten data while maintaining the overall performance of the global model. Additionally, we design a contribution-based client selection algorithm that leverages historical local update information stored on the server, thereby minimizing both computational and communication overhead. We validate our approach through comprehensive experiments on three datasets: MNIST, Fashion-MNIST, and CIFAR-10. These results demonstrate that our approach effectively removes the influence of forgotten data, maintains high global model accuracy, and reduces time overhead by over 60% compared to retraining from scratch.