Interactive Federated AI Training Platform

The Interactive Federated AI Training Platform is a browser-based application that enables users to train and manage machine learning models locally, securely, and intuitively—without the need for backend infrastructure. Built using Vite, React, and TensorFlow.js, the platform supports use cases such as sentiment analysis, text classification, and price prediction. Users can upload their own datasets in CSV format, map the relevant columns, and train models directly in the browser using their device's compute power. The system is designed with privacy in mind: data never leaves the user’s device unless explicitly allowed. Once training is complete, users have the option to download their models, store them in a personal library, or send the model updates to a central backend for aggregation as part of a federated learning loop. The interface includes real-time feedback on training progress, including loss and accuracy logs, model summaries, and prediction previews.

Trained models can be reused, versioned, and deployed through the same interface, supporting continuous learning and experimentation. By combining client-side machine learning with federated learning principles, this platform demonstrates a scalable, privacy-preserving approach to collaborative AI development, making advanced model training more accessible to users across domains without sacrificing data ownership or compliance.