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Robust Federated Learning for Edge Intelligence

In: Handbook of Trustworthy Federated Learning

Author

Listed:
  • 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)

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.

Suggested Citation

  • Dongxiao Yu & Xiao Zhang & Hanshu He & Shuzhen Chen & Jing Qiao & Yangyang Wang & Xiuzhen Cheng, 2025. "Robust Federated Learning for Edge Intelligence," Springer Optimization and Its Applications, in: My T. Thai & Hai N. Phan & Bhavani Thuraisingham (ed.), Handbook of Trustworthy Federated Learning, pages 323-366, Springer.
    • Handle: RePEc:spr:spochp:978-3-031-58923-2_11
    DOI: 10.1007/978-3-031-58923-2_11
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