SPAN LOSS PREDICTION WITH FEDERATED LEARNING

1 . A method 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.

2 . The method of claim 1 , further comprising:

sending the second machine learning model to the plurality of optical systems;

obtaining one or more respective second local model parameters from each respective optical system without obtaining the corresponding respective locally trained second machine learning model; and

generating a third machine learning model by updating the second machine learning model using the obtained second local model parameters.

3 . The method of claim 2 , further comprising:

determining an iteration criterion; and

repeating the steps of claim 2 until the iteration criterion is satisfied.

4 . The method of claim 3 , wherein the iteration criterion includes at least one of: a number of training rounds, a training accuracy of the machine learning model, or a testing accuracy of the machine learning model.

5 . The method of claim 1 , wherein the plurality of optical systems includes reconfigurable optical add-drop multiplexer systems (ROADM systems).

6 . The method of claim 1 , wherein the span losses predicted by the machine learning model include span losses corresponding to at least one of: fiber bending, fiber pinching, or fiber deterioration.

7 . The method of claim 1 , wherein the machine learning models include at least one of: a long short-term memory model, a logistic regression model, or a naive Bayes model.

8 . 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.

9 . The one or more non-transitory computer-readable storage media of claim 8 , further comprising:

sending the second machine learning model to the plurality of optical systems;

obtaining one or more respective second local model parameters from each respective optical system without obtaining the corresponding respective locally trained second machine learning model; and

generating a third machine learning model by updating the second machine learning model using the obtained second local model parameters.

10 . The one or more non-transitory computer-readable storage media of claim 9 , further comprising:

determining an iteration criterion; and

repeating the steps of claim 9 until the iteration criterion is satisfied.

11 . The one or more non-transitory computer-readable storage media of claim 10 , wherein the iteration criterion includes at least one of: a number of training rounds, a training accuracy of the machine learning model, or a testing accuracy of the machine learning model.

12 . The one or more non-transitory computer-readable storage media of claim 8 , wherein the plurality of optical systems includes reconfigurable optical add-drop multiplexer systems (ROADM systems).

13 . The one or more non-transitory computer-readable storage media of claim 8 , wherein the span losses predicted by the machine learning model include span losses corresponding to at least one of: fiber bending, fiber pinching, or fiber deterioration.

14 . The one or more non-transitory computer-readable storage media of claim 8 , wherein the machine learning models include at least one of: a long short-term memory model, a logistic regression model, or a naive Bayes model.

15 . 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.

16 . The system of claim 15 , further comprising:

sending the second machine learning model to the plurality of optical systems;

obtaining one or more respective second local model parameters from each respective optical system without obtaining the corresponding respective locally trained second machine learning model; and

generating a third machine learning model by updating the second machine learning model using the obtained second local model parameters.

17 . The system of claim 16 , further comprising:

determining an iteration criterion; and

repeating the steps of claim 16 until the iteration criterion is satisfied.

18 . The system of claim 17 , wherein the iteration criterion includes at least one of: a number of training rounds, a training accuracy of the machine learning model, or a testing accuracy of the machine learning model.

19 . The system of claim 15 , wherein the plurality of optical systems includes reconfigurable optical add-drop multiplexer systems (ROADM systems).

20 . The system of claim 15 , wherein the span losses predicted by the machine learning model include span losses corresponding to at least one of: fiber bending, fiber pinching, or fiber deterioration.