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Title	Advanced Architectures And Innovative Platforms For Federated Learning: A Comprehensive Exploration
ID_Doc	6478
Authors	Bhati N.; Vyas N.
Year	2024
Published	Model Optimization Methods for Efficient and Edge AI: Federated Learning Architectures, Frameworks and Applications
DOI	http://dx.doi.org/10.1002/9781394219230.ch8
Abstract	This chapter on Federated Learning is a model that values data privacy and decentralized training, and it is the focus of this chapter as it investigates the changing landscape of machine learning. The study of complex neural network-based architectures and hybrid models requires first establishing a firm grounding in the relative merits of centralized and federated techniques. An explanation of the differences between open-source and commercial platforms and how to choose between them follows. Security is given much attention, with topics like homomorphic encryption, differential privacy, and Secure Multiparty Computation covered. Evidence from real-life case studies in fields as diverse as healthcare and smart cities demonstrates Federated Learning's usefulness in the real world. The chapter wraps up by discussing obstacles and projecting future directions, giving the reader a complete picture of where things stand with Federated Learning and where they stand to go. © 2025 The Institute of Electrical and Electronics Engineers, Inc.
Author Keywords	Centralized vs. Federated Approaches; Federated Learning; Homomorphic Encryption; Neural Network-Based Architectures; Open-Source and Commercial Platforms; Secure Multiparty Computation (SMPC)

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