System and methods for self-learning management of next generation networks

A non-transitory machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, the operations comprising:

• collecting data from probes and network elements in a communication network;
• converting the data into semantic vectors using an embedding model;
• generating synthetic data using a generative adversarial network (GANS), wherein the synthetic data is used to test foundation models;
• using the foundation models to generate outputs based on the semantic vectors;
• using federated reinforcement to incorporate human feedback into the semantic vectors;
• and
• managing the communication network based on the outputs.

A method, comprising:

• converting, by a processing system including a processor, data collected from probes and network elements in a communication network into semantic vectors using an embedding model;
• generating, by the processing system, synthetic data using a generative adversarial network (GANS) to train foundation models;
• generating, by the processing system, outputs based on the semantic vectors using the foundation models;
• using, by the processing system, federated reinforcement to incorporate human feedback into the semantic vectors;
• and
• managing the communication network based on the outputs.