mlsanityData to 7 Oct 2026

Secure Byzantine-Robust Machine Learning

arXiv 2006.04747 ↗First posted 8 Jun 2020cs.LG cs.CR stat.MLcited by 18

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Abstract (arXiv)

Increasingly machine learning systems are being deployed to edge servers and devices (e.g. mobile phones) and trained in a collaborative manner. Such distributed/federated/decentralized training raises a number of concerns about the robustness, privacy, and security of the procedure. While extensive work has been done in tackling with robustness, privacy, or security individually, their combination has rarely been studied. In this paper, we propose a secure two-server protocol that offers both input privacy and Byzantine-robustness. In addition, this protocol is communication-efficient, fault-tolerant and enjoys local differential privacy.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jun 2020.

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Cited by year

Citing papers per year: 2021: 7; 2022: 3; 2023: 2; 2024: 3; 2025: 3.

18 papers.

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  1. 2021A Field Guide to Federated Optimizationcited by 312
  2. 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 65
  3. 2021LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learningcited by 63
  4. 2022Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guaranteescited by 30
  5. 2022Byzantine-Robust Decentralized Learning via ClippedGossipcited by 29
  6. 2021Differential Privacy and Byzantine Resilience in SGD: Do They Add Up?cited by 17
  7. 2021Secure Byzantine-Robust Distributed Learning via Clusteringcited by 12
  8. 2024Byzantine-Resilient Secure Aggregation for Federated Learning Without Privacy Compromisescited by 7
  9. 2022Renyi Differential Privacy of Propose-Test-Release and Applications to Private and Robust Machine Learningcited by 4
  10. 2021Combining Differential Privacy and Byzantine Resilience in Distributed SGDcited by 3
  11. 2021PRECAD: Privacy-Preserving and Robust Federated Learning via Crypto-Aided Differential Privacycited by 3
  12. 2024LoByITFL: Low Communication Secure and Private Federated Learningcited by 3
6 more
  1. 2025Practical Framework for Privacy-Preserving and Byzantine-robust Federated Learningcited by 2
  2. 2023Balancing Privacy, Robustness, and Efficiency in Machine Learningcited by 1
  3. 2025DSFL: A Dual-Server Byzantine-Resilient Federated Learning Framework via Group-Based Secure Aggregationcited by 1
  4. 2023SABLE: Secure And Byzantine robust LEarningcited by 0
  5. 2024Brave: Byzantine-Resilient and Privacy-Preserving Peer-to-Peer Federated Learningcited by 0
  6. 2025Perfect Privacy for Discriminator-Based Byzantine-Resilient Federated Learningcited by 0