ZTE had 54 patents in artificial intelligence during Q4 2023. ZTE Corp filed patents for a federated learning participant selection method, model management system, pedestrian tracking method, and optimizing OTN network resources. The methods and apparatuses described in the abstracts focus on selecting participants based on various factors, updating models in wireless communication devices, tracking pedestrians using surveillance cameras, and optimizing network resources through a series of actions and calculations. GlobalData’s report on ZTE gives a 360-degreee view of the company including its patenting strategy. Buy the report here.
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ZTE grant share with artificial intelligence as a theme is 16% in Q4 2023. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Federated learning participant selection method and apparatus, and device and storage medium (Patent ID: US20230394365A1)
The patent filed by ZTE Corp. discloses a method and apparatus for selecting participants in federated learning. The method involves acquiring a group of participants, determining data quality, service, and stability factors for each participant, and selecting a participant based on these factors. The selection process is tailored based on the type of federated learning being conducted, such as Key Performance Indicator (KPI) degradation detection, cell weight optimization, or optical module fault prediction. The selection criteria include parameters like QoS alarm, location data, and in-service duration, with the ultimate goal of optimizing participant selection for efficient federated learning.
The apparatus described in the patent includes a processor and memory storing a program to execute the participant selection method. The method involves acquiring participants, evaluating data quality, service, and stability factors for each participant, and selecting the most suitable participant based on these factors. The selection process is customized based on the type of federated learning being conducted, such as KPI degradation detection, cell weight optimization, or optical module fault prediction. Parameters like QoS alarm, location data, and in-service duration are considered in the selection process to ensure optimal participant selection for effective federated learning.
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