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What is Federated Learning?

Federated learning, also known as collaborative learning, is a machine learning approach where multiple entities train a model locally on their devices without sharing raw data. Instead of centralizing data, only model updates are exchanged, allowing for collaborative model training while maintaining privacy.

How does Federated Learning enhance privacy in machine learning?

Enhancing privacy in machine learning, federated learning ensures that sensitive data remains decentralized and private on individual devices.

By only sharing model updates instead of raw data, federated learning minimizes the risk of exposing personal information during the training process. This approach enables organizations to collaborate on model training without compromising the confidentiality of their data.

What are the key challenges in implementing Federated Learning?

Key challenges in implementing federated learning include network latency, as real-time applications require quick model responses that can be affected by communication delays. Connectivity issues may also arise, as a stable internet connection is necessary for efficient data exchange between devices. Additionally, the iterative training process in federated learning, where updated parameters are exchanged between server and clients multiple times, can introduce complexities in synchronization and coordination among the participating entities.

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