Power Usage at Telco Edge Clusters

A method, comprising:

• processing, by an edge site comprising at least one processor and that is part of a broadband cellular network, telemetry data generated at the edge site a first local artificial intelligence model to produce anonymized embeddings that correspond to the telemetry data, wherein the anonymized embeddings differ from artificial intelligence model gradients;
• sending, by the edge site, the anonymized embeddings to a central scaling platform that aggregates the anonymized embeddings into a group of anonymized embeddings, and that trains a global artificial intelligence model based on the group of anonymized embeddings, to produce trained global artificial intelligence model;
• receiving, by the edge site and from the central scaling platform, an update to a second local artificial intelligence model based on the trained global artificial intelligence model;
• and
• based on an output of the second local artificial intelligence model, implementing, by the edge site, a first operation that modifies at least one parameter applicable to the edge site to increase at least one performance of the edge site or a second operation that modifies the at least one parameter applicable to the edge site to decrease the at least one performance of the edge site.

A non-transitory computer-readable medium comprising instructions that, in response to execution, cause a system comprising at least one processor to perform operations, comprising:

• locally processing data that is generated locally with a first local model to produce anonymized embeddings that correspond to the data, wherein the anonymized embeddings independent of gradients of an artificial intelligence model;
• sending the anonymized embeddings to a remote computing system that aggregates the anonymized embeddings into a group of anonymized embeddings, and that trains a global model based on the group of anonymized embeddings, to produce trained global model;
• receiving, from the remote computing system, an update to a second local model based on the trained global model;
• and
• implementing a local scale up operation or a local scale down operation based on an output of the second local model.