Artificial Intelligence Using Federated Learning Fundamentals Challenges And Applications

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. The book is designed for researchers working in Intelligent Federated Learning and its related applications as well as technology development and is also of interest to academicians data scientists industrial professionals researchers and students. | Artificial Intelligence Using Federated Learning Fundamentals Challenges And Applications