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Understanding the Interplay between Privacy and Robustness in Federated Learning

arXiv 2106.07033 ↗First posted 13 Jun 2021cs.CR cs.LGcited by 0

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Abstract (arXiv)

Federated Learning (FL) is emerging as a promising paradigm of privacy-preserving machine learning, which trains an algorithm across multiple clients without exchanging their data samples. Recent works highlighted several privacy and robustness weaknesses in FL and addressed these concerns using local differential privacy (LDP) and some well-studied methods used in conventional ML, separately. However, it is still not clear how LDP affects adversarial robustness in FL. To fill this gap, this work attempts to develop a comprehensive understanding of the effects of LDP on adversarial robustness in FL. Clarifying the interplay is significant since this is the first step towards a principled design of private and robust FL systems. We certify that local differential privacy has both positive and negative effects on adversarial robustness using theoretical analysis and empirical verification.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 23 references that are in the map. First posted on arXiv 13 Jun 2021.

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References · 16

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4 more
  1. 2020Wireless Federated Learning with Local Differential Privacycited by 50
  2. 2020FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selectioncited by 35
  3. 2019Interpreting and Evaluating Neural Network Robustnesscited by 19
  4. 2019A unified view on differential privacy and robustness to adversarial examplescited by 9

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