Abstract: A federated learning method is provided, applied to the field of artificial intelligence technologies. According to the method, when obtaining models of different network structures, an aggregation node groups models of a same network structure into a same group, and performs parameter aggregation on models in a same group, to obtain a plurality of aggregation models of different network structures. In addition, for each aggregation model, knowledge distillation training is performed on each aggregation model based on the plurality of originally obtained models, to implement experience transfer between the models of different network structures, so as to integrate knowledge and experience of models of various network structures, combine advantages of parameter aggregation and knowledge distillation in integrating model experience, implement aggregation of the models of different network structures, and ensure prediction precision of a model obtained through aggregation.

Abstract: A model training method and an apparatus relate to the field of communication technologies. This can reduce data transmission pressure and improve a training speed and training efficiency when a model is trained via each network node. The method includes: a first node updates an obtained first model to obtain an updated first model, and sends the updated first model to a next-hop node. The first node is any node in a node set, and the node set is used to train the first model. The updated first model converges on the first node. The next-hop node is a node in the node set.

Abstract: A federated learning method includes a first network device that obtains local training data of at least two terminal devices; processes the local training data of the at least two terminal devices, to obtain training datasets; performs model training based on the training datasets, to obtain a model gradient; and sends the model gradient to a second network device. In this way, training data of the first network device is from the at least two terminal devices.

Abstract: Various embodiments of the teachings herein include a method for security monitoring on an OT system. The method may include: determining a time range for calculation on data of the OT system; collecting from the OT system 10, data in the determined time range on a first aspect for security monitoring; calculating, based on data collected, an indicator on the first aspect; and visualizing the indicator on the aspect in a quantitative way.

Abstract: The disclosure discloses a cyberbullying detection method and system. The detection method includes: obtaining a to-be-detected data set, where the to-be-detected data set includes multiple sentence texts of multiple users; classifying the to-be-detected data set by using a classification model based on a bidirectional recurrent neural network, to obtain a probability that each sentence text belongs to cyberbullying; obtaining a sentence text whose probability of belonging to cyberbullying is greater than a specified probability, to obtain a first sentence text set; obtaining an attention value of each sentence text in the first sentence text set and an attention value of each user; detecting, according to the attention value of each sentence text in the first sentence text set and the attention value of each user, whether each sentence text belongs to cyberbullying. The disclosure can achieve a good text classification and identification effect, high accuracy, and a low loss rate.

Abstract: An embodiment of the present invention relates to the technical field of networks, in particular to a testing method, device and system for testing the performance of a Deep Packet Inspection (DPI) device. In this method, a performance analyzing device respectively acquires packet transmission characteristics of packets when being directly forwarded and packet transmission characteristics of the packets having been processed by the DPI device; and a test result on the performance of the DPI device with larger reference significance is acquired by comparing the two packet transmission characteristics.

Abstract: An embodiment of the present invention relates to the technical field of networks, in particular to a testing method, device and system for testing the performance of a Deep Packet Inspection (DPI) device. In this method, a performance analyzing device respectively acquires packet transmission characteristics of packets when being directly forwarded and packet transmission characteristics of the packets having been processed by the DPI device; and a test result on the performance of the DPI device with larger reference significance is acquired by comparing the two packet transmission characteristics.