Model Poisoning

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

3 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

28 shown of 28 papers with code (108 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Ditto: Fair and Robust Federated Learning Through Personalization
  8 Dec 2020 4 repositories listed Syntology ran 15 of 18 samples · 3 unverified · 1 pointer-only (licence)
  Fairness and robustness are two important concerns for federated learning systems.
• Byzantine-robust Federated Learning through Collaborative Malicious Gradient Filtering
  13 Sep 2021 3 repositories listed
  To this end, previous work either makes use of auxiliary data at parameter server to verify the received gradients (e.
• How To Backdoor Federated Learning
  2 Jul 2018 3 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  An attacker selected in a single round of federated learning can cause the global model to immediately reach 100% accuracy on the backdoor task.
• Analyzing Federated Learning through an Adversarial Lens
  29 Nov 2018 2 repositories listed
  Federated learning distributes model training among a multitude of agents, who, guided by privacy concerns, perform training using their local data but share only model parameter updates, for iterative aggregation at…
• Mitigating Sybils in Federated Learning Poisoning
  14 Aug 2018 2 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  Unfortunately, such approaches are susceptible to a variety of attacks, including model poisoning, which is made substantially worse in the presence of sybils.
• Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning
  8 Feb 2025 1 repository listed Syntology ran 0 of 9 samples · 9 unverified
  However, simultaneously addressing both concerns is challenging; secure aggregation facilitates poisoning attacks as most anomaly detection techniques require access to unencrypted local model updates, which are…
• SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning
  6 Feb 2025 1 repository listed
  This paper aims to provide a unified benchmark and analysis of defenses against DPAs and MPAs, clarifying the distinction between these two similar but slightly distinct domains.
• FedSECA: Sign Election and Coordinate-wise Aggregation of Gradients for Byzantine Tolerant Federated Learning
  6 Nov 2024 1 repository listed
  We compare our proposed FedSECA method against 10 robust aggregators under 7 Byzantine attacks on 3 datasets and architectures.
• EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learning
  2 Oct 2024 1 repository listed
  In this paper, we propose a new type of model poisoning attack, EAB-FL, with a focus on exacerbating group unfairness while maintaining a good level of model utility.
• Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated Learning
  20 Jun 2024 1 repository listed
  Then, we use the maximum mean discrepancy (MMD) to calculate the pairwise similarity of the current local model data distribution, its historical data distribution, and global model data distribution.
• A Novel Defense Against Poisoning Attacks on Federated Learning: LayerCAM Augmented with Autoencoder
  2 Jun 2024 1 repository listed
  The autoencoder is designed to process the LayerCAM heat maps from the local model updates, improving their distinctiveness and thereby increasing the accuracy in spotting anomalous maps and malicious local models.
• Leverage Variational Graph Representation For Model Poisoning on Federated Learning
  23 Apr 2024 1 repository listed
  This paper puts forth a new training data-untethered model poisoning (MP) attack on federated learning (FL).
• FedDefender: Client-Side Attack-Tolerant Federated Learning
  18 Jul 2023 1 repository listed
  Evaluations of real-world scenarios across multiple datasets show that the proposed method enhances the robustness of federated learning against model poisoning attacks.
• Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning
  25 Apr 2023 1 repository listed Syntology ran 5 of 11 samples · 6 unverified
  In a federated learning (FL) system, distributed clients upload their local models to a central server to aggregate into a global model.
• Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks
  7 Mar 2023 1 repository listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  Building on existing parameter corruption attacks and refining the Gradient Canceling attack, we perform extensive experiments to confirm our theoretical findings, test the predictability of our transition threshold,…
• Thinking Two Moves Ahead: Anticipating Other Users Improves Backdoor Attacks in Federated Learning
  17 Oct 2022 1 repository listed
  Federated learning is particularly susceptible to model poisoning and backdoor attacks because individual users have direct control over the training data and model updates.
• FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients
  19 Jul 2022 1 repository listed
  FLDetector aims to detect and remove the majority of the malicious clients such that a Byzantine-robust FL method can learn an accurate global model using the remaining clients.
• FedRecAttack: Model Poisoning Attack to Federated Recommendation
  1 Apr 2022 1 repository listed
  Experimental results demonstrate that our proposed FedRecAttack achieves the state-of-the-art effectiveness while its side effects are negligible.
• Semi-Targeted Model Poisoning Attack on Federated Learning via Backward Error Analysis
  22 Mar 2022 1 repository listed
  To overcome this challenge, we propose the Attacking Distance-aware Attack (ADA) to enhance a poisoning attack by finding the optimized target class in the feature space.
• MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clients
  16 Mar 2022 1 repository listed Syntology ran 0 of 8 samples · 8 unverified
  Specifically, we assume the attacker injects fake clients to a federated learning system and sends carefully crafted fake local model updates to the cloud server during training, such that the learnt global model has…
• BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning
  6 Feb 2022 1 repository listed
  This makes it prone to gradient tampering and privacy leakage by a malicious aggregator.
• Towards Understanding Quality Challenges of the Federated Learning for Neural Networks: A First Look from the Lens of Robustness
  5 Jan 2022 1 repository listed
  Therefore, to better understand the current quality status and challenges of these SOTA FL techniques in the presence of attacks and faults, we perform a large-scale empirical study to investigate the SOTA FL's quality…
• SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification
  12 Dec 2021 1 repository listed Syntology ran 4 of 4 samples · 0 unverified · 4 pointer-only (licence)
  Federated learning is inherently vulnerable to model poisoning attacks because its decentralized nature allows attackers to participate with compromised devices.
• ARFED: Attack-Resistant Federated averaging based on outlier elimination
  8 Nov 2021 1 repository listed
  ARFED mainly considers the outlier status of participant updates for each layer of the model architecture based on the distance to the global model.
• FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective
  26 Oct 2021 1 repository listed Syntology ran 4 of 4 samples · 0 unverified · 4 pointer-only (licence)
  Furthermore, we derive a certified robustness guarantee against model poisoning attacks and a convergence guarantee to FedAvg after applying our FL-WBC.
• On the Security Risks of AutoML
  12 Oct 2021 1 repository listed
  Neural Architecture Search (NAS) represents an emerging machine learning (ML) paradigm that automatically searches for models tailored to given tasks, which greatly simplifies the development of ML systems and propels…
• Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning
  23 Aug 2021 1 repository listed
  While recent works have indicated that federated learning (FL) may be vulnerable to poisoning attacks by compromised clients, their real impact on production FL systems is not fully understood.
• Robust Federated Learning with Attack-Adaptive Aggregation
  10 Feb 2021 1 repository listed
  To the best of our knowledge, our aggregation strategy is the first one that can be adapted to defend against various attacks in a data-driven fashion.

Syntology lines on 9 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.