Federated Learning (FL) is an emerging distributed machine learning technology. However, in practical applications, it frequently encounters the challenge of client resource heterogeneity. This can result in long wait times or even model training failures during the communication process of FL. To address this problem, we propose a dynamic hierarchical federated learning mechanism for financial models (DHFM-FLM). The local client adopts a dynamic model training design that leverages the property of resource heterogeneity to enhance the performance of the local model. To avoid prolonged wait times for failing clients, a dynamic communication detection design is proposed at three critical junctures. In each round, the model hierarchical reservation communication design is employed to collect models in segments, thus reducing communication congestion and preventing malicious attacks on the communication process. Experiments with heterogeneous computation and communication resources demonstrate that utilizing the DHFM-FLM boosts model performance by approximately 5-8% and reduces communication time by about 15%. Additionally, DHFM-FLM increases the success rate of the FL task by approximately 6%.

Person Re‐identification (Re‐ID) is aimed at solving the matching problem of the same pedestrian at a different time and in different places. Due to the cross‐device condition, the appearance of different pedestrians may have a high degree of similarity; at this time, using the global features of pedestrians to match often cannot achieve good results. In order to solve these problems, we designed a Spatial Attention Network Guided by Attribute Label (SAN‐GAL), which is a dual‐trace network containing both attribute classification and Re‐ID. Different from the previous approach of simply adding a branch of attribute binary classification network, our SAN‐GAL is mainly divided into two connecting steps. First, with attribute labels as guidance, we generate Attribute Attention Heat map (AAH) through Grad‐CAM algorithm to accurately locate fine‐grained attribute areas of pedestrians. Then, the Attribute Spatial Attention Module (ASAM) is constructed according to the AHH which is taken as the prior knowledge and introduced into the Re‐ID network to assist in the discrimination of the Re‐ID task. In particular, our SAN‐GAL network can integrate the local attribute information and global ID information of pedestrians without introducing additional attribute region annotation, which has good flexibility and adaptability. The test results on Market1501 and DukeMTMC‐reID show that our SAN‐GAL can achieve good results and can achieve 85.8% Rank‐1 accuracy on DukeMTMC‐reID dataset, which is obviously competitive compared with most Re‐ID algorithms.