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Privacy-Utility Trade-Off in Federated Learning

In: Security and Resilience in Distributed Machine Learning

Author

Listed:
  • Kai Li

    (University of Luxembourg, Interdisciplinary Centre for Security, Reliability and Trust (SnT))

  • Xin Yuan

    (Commonwealth Scientific and Industrial Research Organisation (CSIRO), Data61 Business Unit)

  • Wei Ni

    (Commonwealth Scientific and Industrial Research Organisation (CSIRO), Data61 Business Unit)

Abstract

While preserving the privacy of FL, DP inevitably degrades the utility (i.e., accuracy) of FL due to model perturbations caused by DP noise added to model updates [11]. Existing studies have considered exclusively noise with a persistent root-mean-square amplitude and overlooked an opportunity to adjust the amplitudes to alleviate the adverse effects of the noise. This chapter presents a new DP perturbation mechanism with a time-varying noise amplitude to protect the privacy of FL and retain the capability of adjusting the learning performance.

Suggested Citation

  • Kai Li & Xin Yuan & Wei Ni, 2026. "Privacy-Utility Trade-Off in Federated Learning," Springer Series in Reliability Engineering, in: Security and Resilience in Distributed Machine Learning, chapter 9, pages 177-207, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-032-23959-4_9
    DOI: 10.1007/978-3-032-23959-4_9
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