Robust Federated Learning for Edge Intelligence

Dongxiao Yu (), Xiao Zhang (), Hanshu He (), Shuzhen Chen (), Jing Qiao (), Yangyang Wang () and Xiuzhen Cheng ()
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Dongxiao Yu: Shandong University
Xiao Zhang: Shandong University
Hanshu He: Shandong University
Shuzhen Chen: Shandong University
Jing Qiao: Shandong University
Yangyang Wang: Shandong University
Xiuzhen Cheng: Shandong University

A chapter in Handbook of Trustworthy Federated Learning, 2025, pp 323-366 from Springer

Abstract: Abstract Artificial intelligence (AI) has revolutionized various facets of human society and conferred significant advantages to numerous domains, such as entertainment, e-commerce, social media, healthcare, finance, and defense. However, as AI systems are increasingly employed in critical and sensitive scenarios, such as medical diagnosis, financial fraud detection, and military surveillance, the trustworthiness and reliability of the AI models become paramount. It is imperative to ensure the transparency, accountability, and fairness of AI systems to foster their social acceptance and adoption, mitigate their risks and harms, and maximize their benefits and opportunities.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-031-58923-2_11

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DOI: 10.1007/978-3-031-58923-2_11

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