<ul><li>Federated Learning (FL) is a transformative paradigm in the field of distributed machine learning.</li><li>FL enables multiple clients to collaboratively train a shared global model without centralizing sensitive data.</li><li>This survey provides an overview of FL, covering architecture, communication protocol, challenges, and real-world applications.</li><li>It also highlights open research problems and future directions in developing scalable and trustworthy FL systems.</li></ul>

Federated Learning: A Survey on Privacy-Preserving Collaborative Intelligence

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