Federated learning in o-ran

A non-transitory computer-readable storage medium that stores instructions for execution by one or more processors of a non real-time (Non-RT) radio access network intelligence controller (RIC)(Non-RT RIC) in an Open RAN (O-RAN) network, the instructions to configure the one or more processors to perform the following operations:

• download a global machine learning (ML) model to Near-RT RICs;
• upload local ML models from the Near-RT RICs, wherein the local ML models are based on the global ML model;
• update the global ML model based on the local ML models to generate an updated global ML model;
• and
• download the updated global ML model to the Near-RT RICs.

An apparatus for an Non-time (RT) radio access network intelligence controller (RIC)(Non-RT RIC) application (rApp) in an open radio access network (O-RAN), the apparatus comprising:

• memory;
• and, processing circuitry coupled to the memory, the processing circuitry configured to:
• receive a global machine learning (ML) model from a Non-RT RIC;
• update a local ML model based on the global ML model and data collected at the Near-RT RIC to generate an updated local ML model;
• upload the updated local ML model to the Non-RT RIC;
• and
• download an updated global ML model from the Non-RT RIC, wherein the updated global ML model is based on the updated local ML model.