What are the benefits of using federated learning in limiting data shared compared to traditional centralized machine learning approaches?
How does federated learning ensure data privacy and security while allowing multiple parties to collaborate on training models?
What challenges or limitations might arise when implementing federated learning in real-world scenarios, especially in terms of scalability and communication among different devices or servers?
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