mlsanityData to 14 Oct 2026

Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimization

arXiv 2101.11799 ↗First posted 28 Jan 2021cs.LG cs.DCcited by 6

Not on the map. Why?

Abstract (arXiv)

Federated learning (FL), as a type of distributed machine learning frameworks, is vulnerable to external attacks on FL models during parameters transmissions. An attacker in FL may control a number of participant clients, and purposely craft the uploaded model parameters to manipulate system outputs, namely, model poisoning (MP). In this paper, we aim to propose effective MP algorithms to combat state-of-the-art defensive aggregation mechanisms (e.g., Krum and Trimmed mean) implemented at the server without being noticed, i.e., covert MP (CMP). Specifically, we first formulate the MP as an optimization problem by minimizing the Euclidean distance between the manipulated model and designated one, constrained by a defensive aggregation rule. Then, we develop CMP algorithms against different defensive mechanisms based on the solutions of their corresponding optimization problems. Furthermore, to reduce the optimization complexity, we propose low complexity CMP algorithms with a slight performance degradation. In the case that the attacker does not know the defensive aggregation mechanism, we design a blind CMP algorithm, in which the manipulated model will be adjusted properly according to the aggregated model generated by the unknown defensive aggregation. Our experimental results demonstrate that the proposed CMP algorithms are effective and substantially outperform existing attack mechanisms.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 26 of its 51 references that are in the map. First posted on arXiv 28 Jan 2021.

Builds on

26 of 51 references.

References · 26

  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. 2017BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chaincited by 1,106
  5. 2018How To Backdoor Federated Learningcited by 765
  6. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 711
  7. 2018Federated Learning for Mobile Keyboard Predictioncited by 572
  8. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  9. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 520
  10. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 493
  11. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  12. 2018Analyzing Federated Learning through an Adversarial Lenscited by 351
14 more
  1. 2018Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learningcited by 246
  2. 2018Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learningcited by 225
  3. 2017Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimizationcited by 197
  4. 2019Differentially Private Model Publishing for Deep Learningcited by 71
  5. 2019Byzantine-Robust Federated Machine Learning through Adaptive Model Averagingcited by 70
  6. 2017Deep Learning for IoT Big Data and Streaming Analytics: A Surveycited by 59
  7. 2019On Safeguarding Privacy and Security in the Framework of Federated Learningcited by 55
  8. 2017Security Evaluation of Pattern Classifiers under Attackcited by 39
  9. 2019Shielding Collaborative Learning: Mitigating Poisoning Attacks through Client-Side Detectioncited by 26
  10. 2020TIPRDC: Task-Independent Privacy-Respecting Data Crowdsourcing Framework for Deep Learning with Anonymized Intermediate Representationscited by 17
  11. 2020Enabling AI in Future Wireless Networks: A Data Life Cycle Perspectivecited by 14
  12. 2018Privacy Preservation in Location-Based Services: A Novel Metric and Attack Modelcited by 5
  13. 2020RDP-GAN: A R'enyi-Differential Privacy based Generative Adversarial Networkcited by 4
  14. 2018Gaussian Signalling for Covert Communicationscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 3; 2025: 1; 2026: 1.

6 papers.

  1. 2024Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challengescited by 15
  2. 2022Performance Weighting for Robust Federated Learning Against Corrupted Sourcescited by 5
  3. 2024SDBA: A Stealthy and Long-Lasting Durable Backdoor Attack in Federated Learningcited by 2
  4. 2025ZTFed-MAS2S: A Zero-Trust Federated Learning Framework with Verifiable Privacy and Trust-Aware Aggregation for Wind Power Data Imputationcited by 1
  5. 2024SHFL: Secure Hierarchical Federated Learning Framework for Edge Networkscited by 0
  6. 2026EnCAgg: Enhanced Clustering Aggregation for Robust Federated Learning against Dynamic Model Poisoningcited by 0