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8.2 MB
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D3CA8AF3FF218D5427034DB665FC59B84DABCC35
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April 18, 2026, 8:34 p.m.
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(Last updated: April 18, 2026, 8:35 p.m.)
| File | Size |
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| Zhou Y. Federated Edge Learning. Algorithms, Architectures..Trustworthiness 2026.pdf | 8.2 MB |
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44.0 MB
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| Uploaded by indexFroggy | Size 44.0 MB | Health [ 14 /4 ] | Added 2023-07-01 |
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NOTE
SOURCE: Zhou Y. Federated Edge Learning. Algorithms, Architectures..Trustworthiness 2026
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COVER
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MEDIAINFO
Textbook in PDF format This book presents various effective schemes from the perspectives of algorithms, architectures, privacy, and security to enable scalable and trustworthy Federated Edge Learning (FEEL). From the algorithmic perspective, the authors elaborate various federated optimization algorithms, including zeroth-order, first-order, and second-order methods. There is a specific emphasis on presenting provable convergence analysis to illustrate the impact of learning and wireless communication parameters. The convergence rate, computation complexity and communication overhead of the federated zeroth/first/second-order algorithms over wireless networks are elaborated. From the networking architecture perspective, the authors illustrate how the critical challenges of FEEL can be addressed by exploiting different architectures and designing effective communication schemes. Specifically, the communication straggler issue of FEEL can be mitigated by utilizing reconfigurable intelligent surface and unmanned aerial vehicle to reconfigure the propagation environment, while over-the-air computation is utilized to support ultra-fast model aggregation for FEEL by exploiting the waveform superposition property. Additionally, the multi-cell architecture presents a feasible solution for collaborative FEEL training among multiple cells. Finally, the authors discuss the challenges of FEEL from the privacy and security perspective, followed by presenting effective communication schemes that can achieve differentially private model aggregation and Byzantine-resilient model aggregation to achieve trustworthy FEEL. The exponential increase in data volume has driven the proliferation of Artificial Intelligence (AI) applications such as image recognition and natural language processing (NLP), which are made possible by recent advances in Machine Learning (ML) techniques, especially Deep Learning, and the advent of unprecedented computing power. Traditionally, ML processes, including both training and inference, have been supported by cloud computing–centralized cloud data centers offering extensive access to computation, storage, and datasets. However, the rise of intelligent mobile devices and critical applications such as drones, smart vehicles, and augmented reality, necessitate low latency and high privacy, rendering cloud-based ML methodologies inadequate. Consequently, it has become increasingly appealing to keep data local to edge devices and perform training/inference directly at the edge, bypassing the need to transmit data to the cloud or networks. This emerging approach is known as edge AI. The primary challenge lies in the limited computational, storage, energy, and bandwidth resources available to support mobile edge intelligent services. To tackle this issue, recent research has focused on reducing storage overhead, time, and power consumption during inference through model compression methods via hardware and software co-design. Additionally, advanced distributed optimization algorithms have been developed to accelerate the training process by leveraging the computing power and distributed data across multiple devices. This book is designed for researchers and professionals whose focus is wireless communications. Advanced-level students majoring in computer science and electrical engineering will also find this book useful as a reference. Preface Introduction Algorithms First-Order Algorithm for Federated Edge Learning Second-Order Algorithm for Federated Edge Learning Zeroth-Order Algorithm for Federated Edge Learning Architecture GNN for Optimizing RIS-Assisted Federated Edge Learning Federated Edge Learning via Unmanned Aerial Vehicle Federated Edge Learning in Multi-Cell Wireless Networks Security and Privacy Differentially Private Federated Edge Learning Trustworthy Federated Edge Learning via Blockchain
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