Titlebook: Handbook of Trustworthy Federated Learning; My T. Thai,Hai N. Phan,Bhavani Thuraisingham Book 2025 The Editor(s) (if applicable) and The A

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Recent Advances in Federated Graph Learningnce, while locally biased data may lead to overfitting due to limited graph representation. Moreover, the inherent privacy challenges in FL impact the safeguarding of user data. This chapter presents FL and GNNs as separate entities, explaining the core challenges and recent advancements in their in

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Privacy in Federated Learning Natural Language Modelsd evaluation show that our UeDP-Alg outperforms baseline approaches in model utility under the same privacy budget consumption on several NLM tasks, using benchmark datasets. The chapter will continue with discussion about extending UeDP to solve privacy problems in training large language models, i

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Synthetic Data for Privacy Preservation in Distributed Data Analysis SystemsIt reduces data privacy costs, fosters experimentation, enables collaboration, and expedites projects, seamlessly aligning with digital transformation goals. In this chapter, we review the work in this space and describe some of our own recent efforts.

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Toward Green Federated Learningce phases. Further, the scalability of green FL is also of importance for a real-world implementation over a large number of devices. These challenges are coupled with developing efficient learning algorithms under strict resource constraints in both devices and networks. Hence, green FL requires an

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Xiaopeng Jiang,Hessamaldin Mohammadi,Cristian Borcea,NhatHai Phanlack people, this book argues that "race" shaped the contours and possibilities of social mobility in particular ways. This book is critical reading for specialists in the fields of inequality and race, class, 978-3-030-90767-9978-3-030-90765-5

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1931-6828 Privacy) explores the robust defense mechanisms against targeted attacks and addresses fairness concerns, providing a multifaceted foundation for securing Federated Learning systems against evolving threats. P978-3-031-58925-6978-3-031-58923-2Series ISSN 1931-6828 Series E-ISSN 1931-6836

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Dongxiao Yu,Xiao Zhang,Hanshu He,Shuzhen Chen,Jing Qiao,Yangyang Wang,Xiuzhen Chengon and work in shaping the black middle classes both in the past and today. I also examine the contentious relationship between middle-class and lower-class black people and critiques of black leadership, exploring the circumstances under which some research participants become involved in their “co