Federated Learning enables organizations to train robust machine‑learning models across distributed devices without centralizing sensitive data. By orchestrating on‑device training, secure aggregation, differential privacy, and adaptive personalization, you can harness collective insights while maintaining user privacy. This guide outlines five key pillars—system architecture, communication efficiency, privacy guarantees, model convergence, and real‑world deployment considerations—to help you implement federated AI at scale.
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