Federated Learning Unveiled: From Practical Insights to Bold Predictions

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Federated Learning (FL) is a collaborative machine learning technique that lets multiple entities train a shared model without exchanging their data, keeping privacy intact. Over the past decade, FL has evolved, scaling to millions of devices in various domains, all while maintaining strong differential privacy (DP) protections. Companies like Google, Apple, and Meta have successfully brought FL to life in their systems, proving its real-world value. However, challenges remain. Verifying server-side DP guarantees and managing training across a mix of device types are complex issues that need attention. Emerging trends like large multi-modal models and blurred lines between training and personalization are pushing traditional FL to its limits. To address these challenges, we propose a more flexible FL framework focused on privacy principles, using trusted execution environments and open-source collaboration to drive future innovation.