Example – the federated training of an NLP model

The first ML problem that will be converted into an FL scenario through each of the aforementioned FL frameworks will be a classification problem within the domain of NLP. At a high level, NLP refers to the intersection of computational linguistics and ML with an overarching goal of allowing computers to achieve some level of understanding from human language – the details of this understanding vary widely based on the specific problem being targeted.

For this example, we will be performing sentiment analysis on movie reviews, classifying them as positive or negative. The dataset we will be using is the SST-2 dataset (https://nlp.stanford.edu/sentiment/), containing movie reviews in a string format and the associated binary labels 0/1 representing negative and positive sentiment, respectively.

The model we will use to perform binary classification is a pretrained BERT model with a custom classification head. The...