Federated Learning with Hugging Face and Flower for IMDB sentiment (simulated clients)

AI Impact Summary

The content outlines a federated learning workflow for sentiment classification using Hugging Face Transformers (distilBERT) across multiple simulated clients with Flower. It combines Hugging Face datasets for IMDB data, AutoTokenizer and AutoModelForSequenceClassification, and PyTorch constructs to run local training and evaluation, with Flower simulating clients in Colab and FedAvg for aggregation. This approach highlights how to prototype privacy-preserving distributed training and understand data sharding, client synchronization, and convergence behavior before moving to real-world deployments where actual client devices and network conditions will drive performance and reliability decisions.