Federated Learning, Part 2: Implementation with the Flower Framework 🌼

Training models on isolated, incomplete datasets leads to significant failures, as they cannot recognize patterns they have never been exposed to and often misclassify unseen data with high confidence. Federated learning addresses this challenge by enabling collaborative model training across multiple data sources without centralizing the sensitive data itself. The open-source Flower framework simplifies this process by providing an easy way to define client and server applications for distributed training. In this system, a server coordinates by sending a global model to clients, which train locally on their unique data and return updated weights for aggregation, ultimately creating a more robust model.