Federated learning (FL) has become one of the key methods for privacy-preserving collaborative learning. Within the FL framework, an aggregation algorithm is recognized as one of the most crucial components. Existing average aggregation algorithms typically assume that all data have the same importance level. or that weights are based solely on the quantity of data contributed by each client. In contrast, alternative approaches involve training the model locally after aggregation to enhance adaptability. However, these approaches fundamentally ignore the inherent heterogeneity between different clients’ data and the complexity of variations in data at the aggregation stage, which may lead to a suboptimal global model.To address these issues, this study proposes a novel dual-criterion weighted aggregation algorithm involving the quantity and quality of data. Specifically, we quantify the data used for training and perform local model inference accuracy evaluation on a specialized dataset to assess the data quality of each client. These two factors are utilized as weights within the aggregation process, applied through a dynamically weighted summation of these two factors. This approach allows the algorithm to adaptively adjust the weights, ensuring that every client can contribute to the global model, regardless of their data’s size or initial quality. Our experiments show that the proposed algorithm outperforms several existing state-of-the-art aggregation approaches on both a general-purpose open-source dataset, CIFAR-10, and a dataset specific to visual obstacle avoidance.