mlsanityData to 7 Oct 2026

Byzantine-Resilient Secure Aggregation for Federated Learning Without Privacy Compromises

arXiv 2405.08698 ↗First posted 14 May 2024cs.IT cs.CR cs.DCcited by 7

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Abstract (arXiv)

Federated learning (FL) shows great promise in large scale machine learning, but brings new risks in terms of privacy and security. We propose ByITFL, a novel scheme for FL that provides resilience against Byzantine users while keeping the users' data private from the federator and private from other users. The scheme builds on the preexisting non-private FLTrust scheme, which tolerates malicious users through trust scores (TS) that attenuate or amplify the users' gradients. The trust scores are based on the ReLU function, which we approximate by a polynomial. The distributed and privacy-preserving computation in ByITFL is designed using a combination of Lagrange coded computing, verifiable secret sharing and re-randomization steps. ByITFL is the first Byzantine resilient scheme for FL with full information-theoretic privacy.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 13 of its 30 references that are in the map. First posted on arXiv 14 May 2024.

Builds on

13 of 30 references.

References · 13

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Deep Leakage from Gradientscited by 1,004
  3. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 707
  4. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 698
  5. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 479
  6. 2020FLTrust: Byzantine-robust Federated Learning via Trust Bootstrappingcited by 330
  7. 2018The Hidden Vulnerability of Distributed Learning in Byzantiumcited by 324
  8. 2020Byzantine-Resilient Secure Federated Learningcited by 93
  9. 2018Lagrange Coded Computing: Optimal Design for Resiliency, Security and Privacycited by 66
  10. 2022The Fundamental Price of Secure Aggregation in Differentially Private Federated Learningcited by 39
  11. 2020Secure Byzantine-Robust Machine Learningcited by 18
  12. 2023FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregationcited by 16
1 more
  1. 2021Secure Byzantine-Robust Distributed Learning via Clusteringcited by 12

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 2.

7 papers.

  1. 2024Communication-Efficient Byzantine-Resilient Federated Zero-Order Optimizationcited by 4
  2. 2024LoByITFL: Low Communication Secure and Private Federated Learningcited by 3
  3. 2025Private Aggregation for Byzantine-Resilient Heterogeneous Federated Learningcited by 0
  4. 2025Perfect Privacy for Discriminator-Based Byzantine-Resilient Federated Learningcited by 0
  5. 2025Beyond Trade-offs: A Unified Framework for Privacy, Robustness, and Communication Efficiency in Federated Learningcited by 0
  6. 2026Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversariescited by 0
  7. 2026FL-OA: A Byzantine-Robust Federated Learning Framework with Outsourced Auditing for Intelligent Devicescited by 0