mlsanityData to 14 Oct 2026

RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation

arXiv 2405.15182 ↗First posted 24 May 2024cs.CR cs.AIcited by 10

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Abstract (arXiv)

Federated learning (FL) allows multiple devices to train a model collaboratively without sharing their data. Despite its benefits, FL is vulnerable to privacy leakage and poisoning attacks. To address the privacy concern, secure aggregation (SecAgg) is often used to obtain the aggregation of gradients on sever without inspecting individual user updates. Unfortunately, existing defense strategies against poisoning attacks rely on the analysis of local updates in plaintext, making them incompatible with SecAgg. To reconcile the conflicts, we propose a robust federated learning framework against poisoning attacks (RFLPA) based on SecAgg protocol. Our framework computes the cosine similarity between local updates and server updates to conduct robust aggregation. Furthermore, we leverage verifiable packed Shamir secret sharing to achieve reduced communication cost of O(M+N) per user, and design a novel dot product aggregation algorithm to resolve the issue of increased information leakage. Our experimental results show that RFLPA significantly reduces communication and computation overhead by over 75% compared to the state-of-the-art secret sharing method, BREA, while maintaining competitive accuracy.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 27 of its 57 references that are in the map. First posted on arXiv 24 May 2024.

Builds on

27 of 57 references.

References · 27

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  4. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
  5. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  6. 2019Deep Leakage from Gradientscited by 1,021
  7. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  8. 2018How To Backdoor Federated Learningcited by 765
  9. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 711
  10. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 520
  11. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 493
  12. 2020FLTrust: Byzantine-robust Federated Learning via Trust Bootstrappingcited by 346
15 more
  1. 2018The Hidden Vulnerability of Distributed Learning in Byzantiumcited by 329
  2. 2019Robust Aggregation for Federated Learningcited by 317
  3. 2021No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Datacited by 241
  4. 2021The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregationcited by 128
  5. 2024OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learningcited by 128
  6. 2019Secure Federated Matrix Factorizationcited by 102
  7. 2020Byzantine-Resilient Secure Federated Learningcited by 93
  8. 2020Local and Central Differential Privacy for Robustness and Privacy in Federated Learningcited by 78
  9. 2023FedDisco: Federated Learning with Discrepancy-Aware Collaborationcited by 67
  10. 2021Challenges and Approaches for Mitigating Byzantine Attacks in Federated Learningcited by 47
  11. 2016Collecting and Analyzing Data from Smart Device Users with Local Differential Privacycited by 25
  12. 2021RoFL: Robustness of Secure Federated Learningcited by 25
  13. 2023Secure Federated Learning against Model Poisoning Attacks via Client Filteringcited by 8
  14. 2023Fake It Till Make It: Federated Learning with Consensus-Oriented Generationcited by 6
  15. 2013On the Communication Complexity of Secure Computationcited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 6; 2026: 1.

10 papers.

  1. 2024FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Modelscited by 30
  2. 2024Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Modelscited by 18
  3. 2025Federated Learning for Cross-Domain Data Privacy: A Distributed Approach to Secure Collaborationcited by 9
  4. 2025Capture Global Feature Statistics for One-Shot Federated Learningcited by 6
  5. 2025Wavelet Scattering Transform and Fourier Representation for Offline Detection of Malicious Clients in Federated Learningcited by 2
  6. 2024How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solutioncited by 0
  7. 2025FLTG: Byzantine-Robust Federated Learning via Angle-Based Defense and Non-IID-Aware Weightingcited by 0
  8. 2025Adaptive Federated Few-Shot Rare-Disease Diagnosis with Energy-Aware Secure Aggregationcited by 0
  9. 2025LSHFed: Robust and Communication-Efficient Federated Learning with Locally-Sensitive Hashing Gradient Mappingcited by 0
  10. 2026Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversariescited by 0