<ul><li>Federated Learning (FL) aims to find parameters that minimize the global objective, which combines local objectives weighted by data size.</li><li>Federated Averaging (FedAvg) is a fundamental FL algorithm that operates in rounds, where the server sends the current model to selected clients for local training.</li><li>FedAvg averages the updates from clients' local training, weighted by data size, to update the global model and reduce communication costs.</li><li>Pairing FL with techniques like Differential Privacy (DP) and secure computation enhances its robustness and potential for becoming a mainstream machine learning tool in the future.</li></ul>

Federated Learning in Production: A Practical Guide

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