mlsanityData to 14 Oct 2026

Secure Byzantine-Robust Distributed Learning via Clustering

arXiv 2110.02940 ↗First posted 6 Oct 2021cs.CR cs.AI cs.LGcited by 12

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Abstract (arXiv)

Federated learning systems that jointly preserve Byzantine robustness and privacy have remained an open problem. Robust aggregation, the standard defense for Byzantine attacks, generally requires server access to individual updates or nonlinear computation -- thus is incompatible with privacy-preserving methods such as secure aggregation via multiparty computation. To this end, we propose SHARE (Secure Hierarchical Robust Aggregation), a distributed learning framework designed to cryptographically preserve client update privacy and robustness to Byzantine adversaries simultaneously. The key idea is to incorporate secure averaging among randomly clustered clients before filtering malicious updates through robust aggregation. Experiments show that SHARE has similar robustness guarantees as existing techniques while enhancing privacy.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 14 of its 25 references that are in the map. First posted on arXiv 6 Oct 2021.

Builds on

14 of 25 references.

References · 14

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  5. 2020Adaptive Federated Optimizationcited by 1,046
  6. 2019Towards Federated Learning at Scale: System Designcited by 832
  7. 2018LEAF: A Benchmark for Federated Settingscited by 792
  8. 2018Federated Learning for Mobile Keyboard Predictioncited by 572
  9. 2019Robust Aggregation for Federated Learningcited by 317
  10. 2019Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulationcited by 141
  11. 2019Robust Federated Learning in a Heterogeneous Environmentcited by 90
  12. 2019Zeno++: Robust Fully Asynchronous SGDcited by 53
2 more
  1. 2020Secure Byzantine-Robust Machine Learningcited by 19
  2. 2020Towards Bidirectional Protection in Federated Learningcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 4; 2024: 3; 2025: 1; 2026: 1.

12 papers.

  1. 2022Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guaranteescited by 30
  2. 2024Byzantine-Resilient Secure Aggregation for Federated Learning Without Privacy Compromisescited by 7
  3. 2022zPROBE: Zero Peek Robustness Checks for Federated Learningcited by 6
  4. 2023ByzSecAgg: A Byzantine-Resistant Secure Aggregation Scheme for Federated Learning Based on Coded Computing and Vector Commitmentcited by 4
  5. 2023FedREP: A Byzantine-Robust, Communication-Efficient and Privacy-Preserving Framework for Federated Learningcited by 4
  6. 2021PRECAD: Privacy-Preserving and Robust Federated Learning via Crypto-Aided Differential Privacycited by 3
  7. 2024LoByITFL: Low Communication Secure and Private Federated Learningcited by 3
  8. 2023Inferring Communities of Interest in Collaborative Learning-based Recommender Systemscited by 1
  9. 2025Perfect Privacy for Discriminator-Based Byzantine-Resilient Federated Learningcited by 1
  10. 2023SABLE: Secure And Byzantine robust LEarningcited by 0
  11. 2024Brave: Byzantine-Resilient and Privacy-Preserving Peer-to-Peer Federated Learningcited by 0
  12. 2026Giskard : Byzantine Robust and Confidential Aggregation for Large-Scale Decentralized Learningcited by 0