Federated Machine Learning is a novel approach to combining distributed Machine Learning, cryptography, security, and incentive mechanism design. It allows organizations to keep sensitive and private data on users or customers decentralized and secure, helping them comply with stringent data protection regulations like GDPR and CCPA. Artificial Intelligence Using Federated Learning: Fundamentals, Challenges, and Applications enables training AI models on a large number of decentralized devices or servers, making it a scalable and efficient solution. It also allows organizations to create more versatile AI models by training them on data from diverse sources or domains. This approach can unlock innovative use cases in fields like healthcare, finance, and IoT, where data privacy is paramount. Federated Learning operates by training local models on diverse datasets held by individual nodes, with only model parameters exchanged to create a global model. Unlike traditional centralized Machine Learning, Federated Learning eliminates the need to aggregate data in one location, thereby reducing privacy concerns associated with data sharing.

Artificial Intelligence Using FL PDF

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