To address the problems of low recognition precision and difficult recognition of the existing one-stage anchor-free detectors in genetic object detection scenarios， a high-precision object detection algorithm based on improved variable focal network VarifocalNet （VFNet） was proposed. Firstly， the ResNet backbone network used for feature extraction in VFNet was replaced by the Recurrent Layer Aggregation Network （RLANet）. The recurrent residual connection operation imported the features of the previous layer into the subsequent network layer to improve the representation ability of the features. Next， the original feature fusion network was substituted by the Feature Pyramid Network （FPN） with feature alignment convolution operation， thereby effectively utilizing the deformable convolution operation in the fusion process of the upper and lower layers of FPN to align the features and optimize the feature quality. Finally， the Focal-Global Distillation （FGD） algorithm was used to further improve the detection performance of small-scale algorithm. The evaluation experimental results on COCO （Common Objects in Context） 2017 dataset show that under the same training conditions，the improved algorithm adopting RLANet-50 as the backbone can achieve the mean Average Precision （mAP） of 45.9%， which is 4.3 percentage points higher than that of the VFNet algorithm， and the improved algorithm has the number of parameters of 36.67×10 ⁶， which is only 4×10 ⁶ higher than that of the VFNet algorithm. The improved VFNet algorithm only slightly increases the amount of parameters while improving the detection accuracy， indicating that the algorithm can meet the requirements of lightweight and high-precision of object detection.

Federated Learning （FL） emerges as a novel privacy-preserving Machine Learning （ML） paradigm. However， the distributed training structure of FL is more vulnerable to poisoning attack， where adversaries contaminate the global model through uploading poisoning models， resulting in the convergence deceleration and the prediction accuracy degradation of the global model. To solve the above problem， a poisoning attack detection scheme based on Generative Adversarial Network （GAN） was proposed. Firstly， the benign local models were fed into the GAN to output testing samples. Then， the testing samples were used to detect the local models uploaded by the clients. Finally， the poisoning models were eliminated according to the testing metrics. Meanwhile， two test metrics named F1 score loss and accuracy loss were defined to detect the poisoning models and extend the detection scope from one single type of poisoning attacks to all types of poisoning attacks. Besides， a threshold determination method was designed to deal with misjudgment， so that the robust of misjudgment was confirmed. Experimental results on MNIST and Fashion-MNIST datasets show that the proposed scheme can generate high-quality testing samples， and then detect and eliminate poisoning models. Compared with the global models trained with the detection scheme based on directly gathering test data from clients and the detection scheme based on generating test data and using test accuracy as the test metric， the global model trained with the proposed scheme has significant accuracy improvement from 2.7 to 12.2 percentage points.