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Book
Trustworthy Federated Learning : First International Workshop, FL 2022, Held in Conjunction with IJCAI 2022, Vienna, Austria, July 23, 2022, Revised Selected Papers
Authors: --- --- --- ---
ISBN: 3031289951 303128996X Year: 2023 Publisher: Cham Springer International Publishing AG

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Digital
Trustworthy Federated Learning : First International Workshop, FL 2022, Held in Conjunction with IJCAI 2022, Vienna, Austria, July 23, 2022, Revised Selected Papers
Authors: --- --- --- ---
ISBN: 9783031289965 9783031289958 9783031289972 Year: 2023 Publisher: Cham Springer International Publishing, Imprint: Springer


Book
Federated Learning Over Wireless Edge Networks
Authors: --- --- --- --- --- et al.
ISBN: 9783031078385 Year: 2022 Publisher: Cham Springer International Publishing :Imprint: Springer

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Book
Trustworthy Federated Learning
Authors: --- --- --- --- --- et al.
ISBN: 9783031289965 Year: 2023 Publisher: Cham Springer International Publishing :Imprint: Springer

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This book constitutes the refereed proceedings of the First International Workshop, FL 2022, Held in Conjunction with IJCAI 2022, held in Vienna, Austria, during July 23-25, 2022. The 11 full papers presented in this book were carefully reviewed and selected from 12 submissions. They are organized in three topical sections: answer set programming; adaptive expert models for personalization in federated learning and privacy-preserving federated cross-domain social recommendation.


Multi
Federated Learning Over Wireless Edge Networks
Authors: --- --- --- --- --- et al.
ISBN: 9783031078385 9783031078378 9783031078392 9783031078408 Year: 2022 Publisher: Cham Springer International Publishing :Imprint: Springer

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This book first presents a tutorial on Federated Learning (FL) and its role in enabling Edge Intelligence over wireless edge networks. This provides readers with a concise introduction to the challenges and state-of-the-art approaches towards implementing FL over the wireless edge network. Then, in consideration of resource heterogeneity at the network edge, the authors provide multifaceted solutions at the intersection of network economics, game theory, and machine learning towards improving the efficiency of resource allocation for FL over the wireless edge networks. A clear understanding of such issues and the presented theoretical studies will serve to guide practitioners and researchers in implementing resource-efficient FL systems and solving the open issues in FL respectively. Provides a concise introduction to Federated Learning (FL) and how it enables Edge Intelligence; Highlights the challenges inherent to achieving scalable implementation of FL at the wireless edge; Presents how FL can address challenges resulting from the confluence of AI and wireless communications.


Book
Integrating Edge Intelligence and Blockchain
Authors: --- --- --- --- --- et al.
ISBN: 9783031101861 9783031101854 9783031101878 9783031101885 Year: 2022 Publisher: Cham Springer International Publishing :Imprint: Springer

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Integrating Edge Intelligence and Blockchain
Authors: --- --- --- --- --- et al.
ISBN: 9783031101861 9783031101854 9783031101878 9783031101885 Year: 2022 Publisher: Cham Springer International Publishing :Imprint: Springer

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This book examines whether the integration of edge intelligence (EI) and blockchain (BC) can open up new horizons for providing ubiquitous intelligent services. Accordingly, the authors conduct a summarization of the recent research efforts on the existing works for EI and BC, further painting a comprehensive picture of the limitation of EI and why BC could benefit EI. To examine how to integrate EI and BC, the authors discuss the BC-driven EI and tailoring BC to EI, including an overview, motivations, and integrated frameworks. Finally, some challenges and future directions are explored. The book explores the technologies associated with the integrated system between EI and BC, and further bridges the gap between immature BC and EI-amicable BC. Explores the integration of edge intelligence (EI) and blockchain (BC), including their integrated motivations, frameworks and challenges; Presents how BC-driven EI can realize computing-power management, data administration, and model optimization; Describes how to tailor BC to better support EI, including flexible consensus protocol, effective incentive mechanism, intellectuality smart contract, and scalable BC system tailoring; Presents some key research challenges and future directions for the integrated system.

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