mlsanityData to 7 Oct 2026

Network-Level Adversaries in Federated Learning

arXiv 2208.12911 ↗First posted 27 Aug 2022cs.CR cs.LG cs.NIcited by 4

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Abstract (arXiv)

Federated learning is a popular strategy for training models on distributed, sensitive data, while preserving data privacy. Prior work identified a range of security threats on federated learning protocols that poison the data or the model. However, federated learning is a networked system where the communication between clients and server plays a critical role for the learning task performance. We highlight how communication introduces another vulnerability surface in federated learning and study the impact of network-level adversaries on training federated learning models. We show that attackers dropping the network traffic from carefully selected clients can significantly decrease model accuracy on a target population. Moreover, we show that a coordinated poisoning campaign from a few clients can amplify the dropping attacks. Finally, we develop a server-side defense which mitigates the impact of our attacks by identifying and up-sampling clients likely to positively contribute towards target accuracy. We comprehensively evaluate our attacks and defenses on three datasets, assuming encrypted communication channels and attackers with partial visibility of the network.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 20 of its 44 references that are in the map. First posted on arXiv 27 Aug 2022.

Builds on

20 of 44 references.

References · 20

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 836
  5. 2018How To Backdoor Federated Learningcited by 745
  6. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 698
  7. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 479
  8. 2020FLTrust: Byzantine-robust Federated Learning via Trust Bootstrappingcited by 330
  9. 2018The Hidden Vulnerability of Distributed Learning in Byzantiumcited by 324
  10. 2020Attack of the Tails: Yes, You Really Can Backdoor Federated Learningcited by 307
  11. 2020Data Poisoning Attacks Against Federated Learning Systemscited by 266
  12. 2019Can You Really Backdoor Federated Learning?cited by 250
8 more
  1. 2020On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shapingcited by 82
  2. 2018Byzantine Stochastic Gradient Descentcited by 81
  3. 2020Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning)cited by 54
  4. 2021FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspectivecited by 35
  5. 2021Byzantine-robust Federated Learning through Collaborative Malicious Gradient Filteringcited by 28
  6. 2021DeSMP: Differential Privacy-exploited Stealthy Model Poisoning Attacks in Federated Learningcited by 11
  7. 2020Byzantine Fault-Tolerant Distributed Machine Learning Using Stochastic Gradient Descent (SGD) and Norm-Based Comparative Gradient Elimination (CGE)cited by 4
  8. 2020Structural Attacks on Local Routing in Payment Channel Networkscited by 1

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Cited by year

Citing papers per year: 2023: 4.

4 papers.

  1. 2023Backdoor Attacks in Peer-to-Peer Federated Learningcited by 9
  2. 2023Can Decentralized Learning be more robust than Federated Learning?cited by 6
  3. 2023Poisoning Attacks in Federated Edge Learning for Digital Twin 6G-enabled IoTs: An Anticipatory Studycited by 3
  4. 2023SureFED: Robust Federated Learning via Uncertainty-Aware Inward and Outward Inspectioncited by 3