FedSense: Federated Sentiment Analysis with Privacy Guarantees

FedSense, federated sentiment analysis with privacy guarantees.

FedSense, federated sentiment analysis with privacy guarantees. This reference project walks through the whole job, from the problem and the design to the build and the evaluation, so that a developer can study how it works, run it and extend it.

It suits learners who want to work with language models and text or speech: preparing text data, using or fine-tuning a model, evaluating it and building an interface around it.

The main parts are privacy-preserving training across several clients, with only model updates shared, security and privacy controls such as encryption and access control, text or speech data preparation, model training, fine-tuning or prompting, and evaluation with task-appropriate metrics. The reference build uses Flower, Python, Hugging Face Transformers, spaCy, and PyTorch, with a clear project structure, setup instructions and sample data so that it can be run and extended.