The application relates to an NR-U-based power internet federal learning efficient
access method, which comprises utilizing a
point coordination function (PCF) and proposing a WiFi and NR-U friendly coexistence mechanism; according to the proposed WiFi and NR-U friendly coexistence mechanism, a federal learning
system model running on NR-U is established, the
system model comprises one central
server and K power
internet of things terminals, model training is carried out between the central
server and the power terminals in a contention-free period (CFP), and mutual communication is completed through NR-U; according to the established
system model, a joint
power equipment selection and
bandwidth allocation optimization method is designed, efficient model training and low-
delay communication under large-scale
power equipment access are realized under the guarantee of the performance requirements of WiFi networks and NU-R networks. The large-scale power internet equipment access capability is improved, the intelligent
service demand of the power internet is met, low-
delay communication and efficient training of the central
server and the terminal power internet equipment are realized under the guarantee of the performance of NR-U and WiFi networks.