Span loss prediction with federated learning

One or more non-transitory computer-readable storage media configured to store instructions that, in response to being executed, cause a system to perform operations, the operations comprising:

• sending a first machine learning model to a plurality of optical systems in which the first machine learning model is configured to predict span losses in optical systems and in which each respective optical system of the plurality of optical systems is configured to:
• identify the span losses associated with the respective optical system;
• and
• locally train the first machine learning model according to the respective identified span losses associated with the respective optical system to obtain a respective local first machine learning model that includes one or more respective local model parameters that predict the respective span losses of the respective optical system;
• obtaining the respective local model parameters from each respective optical system without obtaining the corresponding respective locally trained first machine learning model;
• generating a second machine learning model by updating the first machine learning model using the obtained local model parameters;
• and
• predicting, by the second machine learning model, occurrences of one or more span losses corresponding to a given optical system.

A system comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable storage media configured to store instructions that, in response to being executed, cause a system to perform operations, the operations comprising:
• sending a first machine learning model to a plurality of optical systems in which the first machine learning model is configured to predict span losses in optical systems and in which each respective optical system of the plurality of optical systems is configured to:
• identify the span losses associated with the respective optical system;
• and
• locally train the first machine learning model according to the respective identified span losses associated with the respective optical system to obtain a respective local first machine learning model that includes one or more respective local model parameters that predict the respective span losses of the respective optical system;
• obtaining the respective local model parameters from each respective optical system without obtaining the corresponding respective locally trained first machine learning model;
• generating a second machine learning model by updating the first machine learning model using the obtained local model parameters;
• and
• predicting, by the second machine learning model, occurrences of one or more span losses corresponding to a given optical system.