Introductory Chapter: Federated Learning – Bridging the Gap between Privacy and Collaboration in Machine Learning
Abstract
Introduction to federated learning: A paradigm shift in data privacyA distributed machine learning (ML) framework is called federated learning (FL).Under the control of the central server, several clients work together in FL to solve traditional distributed machine learning tasks without disclosing their local private information to third parties.Federated learning functions without requiring cloud storage of user data.Federated learning guarantees privacy while enabling smarter models, reduced latency, and less power use.Numerous algorithmic and computational challenges had to be solved before federated learning could be implemented.This book mostly uses machine learning and deep learning to classify federated learnings.This book first describes the definition, architecture, classification, and development process of FL and provides an explanation of the concept through a comparison with traditional distributed learning.After that, it lists common FL issues that must be resolved.Several federated machine learning algorithms, with a focus on deep learning and classification, are briefly explained and compared, building upon the foundation of classical federated learnings algorithms.Lastly, the breadth of FL based on deep learning and potential future advances are covered in this book.In the age of AI, collaborative learning, particularly the exchange of data among various institutions, can significantly enhance the development of robust AI models.In standard machine…
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