mlsanityData to 7 Oct 2026

LoByITFL: Low Communication Secure and Private Federated Learning

arXiv 2405.19217 ↗First posted 29 May 2024cs.IT cs.CR cs.DCcited by 3

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Abstract (arXiv)

Privacy of the clients' data and security against Byzantine clients are key challenges in Federated Learning (FL). Existing solutions to joint privacy and security incur sacrifices on the privacy guarantee. We introduce LoByITFL, the first communication-efficient information-theoretically private and secure FL scheme that makes no sacrifices on the privacy guarantees while ensuring security against Byzantine adversaries. The key components are a small and representative dataset available to the federator, a careful modification of the FLTrust algorithm, and the one-time use of a trusted third party during an initialization period. We provide theoretical guarantees on the privacy and Byzantine resilience, as well as experimental results showing the convergence of LoByITFL.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 16 of its 45 references that are in the map. First posted on arXiv 29 May 2024.

Builds on

16 of 45 references.

References · 16

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Deep Leakage from Gradientscited by 1,004
  3. 2018How To Backdoor Federated Learningcited by 745
  4. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 707
  5. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 698
  6. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 479
  7. 2020FLTrust: Byzantine-robust Federated Learning via Trust Bootstrappingcited by 330
  8. 2018The Hidden Vulnerability of Distributed Learning in Byzantiumcited by 324
  9. 2020Byzantine-Resilient Secure Federated Learningcited by 93
  10. 2018Lagrange Coded Computing: Optimal Design for Resiliency, Security and Privacycited by 66
  11. 2020Secure Byzantine-Robust Machine Learningcited by 18
  12. 2023FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregationcited by 16
4 more
  1. 2021Secure Byzantine-Robust Distributed Learning via Clusteringcited by 12
  2. 2022ScionFL: Efficient and Robust Secure Quantized Aggregationcited by 8
  3. 2024Byzantine-Resilient Secure Aggregation for Federated Learning Without Privacy Compromisescited by 7
  4. 2024Privacy-Preserving Aggregation for Decentralized Learning with Byzantine-Robustnesscited by 4

Built on by

Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025Private Aggregation for Byzantine-Resilient Heterogeneous Federated Learningcited by 0
  2. 2025Perfect Privacy for Discriminator-Based Byzantine-Resilient Federated Learningcited by 0
  3. 2025Beyond Trade-offs: A Unified Framework for Privacy, Robustness, and Communication Efficiency in Federated Learningcited by 0