Robust Federated Learning Frameworks Guarding Against Data Flipping Threats for Autonomous Vehicles
Federated Learning;Data Poisoning;Adversarial Attack
Yes
No ethics review needed.
typos:
1. Page 2, line 87 "labels.,Furthermore,", "the robustness,of the", line 90 "high classification performance..."
1. Again data poisoning attack in FL and Autonomous Vehicle is important.
2. The proposed method (PCA+SVM) can be used for detecting outliers.
3. Experiments are provided.
The paper proposes new defense method on against data poisoning attacks in federated learning, especially for image classification models in autonomous vehicles. The method uses Principal Component Analysis (PCA) and Multi-class Support Vector Machine (SVM) classifiers together to offer a strong way to defend against label flipping attacks in federated learning. Experiments show the performance.
1. PCA+SVM has been well studied and has been extended to FL settings. The contribution of the paper is unclear.
2. Experiments do not have comparison methods.
3. It does not have experiments on simulated autonomous vehicles.
Yes
No ethics review needed.
1) Have you considered methods to reduce the computational burden of PCA-SVM for a larger number of participants? Could dimensionality reduction or distributed processing techniques make scaling feasible?

2) How do you anticipate that the PCA-SVM method would perform against other common attacks in FL, such as backdoor or gradient leakage attacks?

3) How does the PCA-SVM mechanism perform when faced with non-IID data, which is common in real federated learning applications?
By focusing on label-flipping in AVs, this paper addresses a specific and underexplored threat in FL. The experimental design is robust, testing on multiple datasets and using meaningful metrics to demonstrate the effectiveness of the defense. Clear explanations of PCA and SVM integration, along with structured result presentation and well-contextualized related work, enhance the paper’s readability and situate its contributions within the broader FL literature.
The authors propose a novel defense strategy that combines PCA and SVM to detect and mitigate these attacks. This approach aims to filter out malicious updates by identifying statistical anomalies in model updates before they impact the global model. Their experiments, conducted using various datasets, demonstrate that this defense mechanism successfully maintains model accuracy and integrity, even with significant data poisoning levels.
1) The study’s scope is restricted to only three participants due to computational limitations, which limits the generalizability of its findings. Scaling up to a larger number of clients would better reflect real-world FL systems in autonomous vehicles.

2) The focus on label-flipping attacks is a good starting point, but broader testing on other attack types, such as backdoor attacks or noise injection, would make the proposed defense mechanism more comprehensive.

3) Although the paper provides context on existing methods, many comparisons with other recent FL defense strategies are missing, such as hierarchical FL or advanced anomaly detection models. It would be better to Include a comparison of metrics like detection accuracy, computational cost, and resilience against adversarial attacks.
Yes
No ethics review needed.
1. What challenges arise in applying this label-flipping attack and its corresponding defense in the federated learning setting?
2. What advantages do the proposed attack and defense offer over existing methods?
1. Interesting to explore the poisoning attacks in the FL setting, for ensuring the security of AVs.
2. The logic of the proposal is easy to follow, although there are a lots of grammar issues and typos.
3. Experiment results to some extent support their conclusion.
This paper aims to resist data poisoning attacks (in particular data-flipping) faced by autonomous vehicles (AVs) in the federated learning (FL) setting. The authors propose a novel defense mechanism that combines anomaly detection with robust aggregation techniques. They employ statistical outlier detection and model-based consistency checks to filter out compromised updates before they affect the global model.
The contributions of the paper are not solid enough, and the challenges are not adequately highlighted. It appears that the authors merely combine existing technologies into different scenarios.

On one hand, while the authors use a label-flipping attack to implement targeted data poisoning in federated learning (FL), they do not clarify how this attack differs from existing ones or specify the challenges it poses in the FL context.

On the other hand, in their proposed defense mechanism, they set a detection agent in each autonomous vehicle (AV) to filter out attacking data. However, this defense mechanism is independent of the federated learning setting, meaning that existing defense strategies could be directly applied here for protection.

The relevance to AVs is not clear. It is difficult to find the connection between the proposals and AVs, despite the authors providing Figure 1 to illustrate the FL for label-flipping attacks on AVs.

Additionally, no comparisons are provided. A comparison of attacks and defenses should be included to strengthen the contributions. Without this, it is challenging to assess the effectiveness of the proposed attack and defense mechanisms.

Finally, there are too many unexpected grammar issues and typos that make the current version far from acceptable.
Yes
Not applicable.
No ethics review needed.
Please check the weakness part.
+ The problem worths detailed investigation. 

+ The structure of the draft is good.
This paper proposes a defense for Federated Learning (FL) against data poisoning attacks, especially in autonomous vehicles. The authors use PCA and SVM to detect label flipping attacks and try to build a robust FL framework.
- Techniques: 
 1. Label Flippinp: Label flipping is the easiest data poisoning, there are many poisoning attacks like backdoor or model poisoning attack, this threat seems to be quite weak as of now. 
 2. No Baselines for a comparative study
 3. Insufficient experiments
No ablation studies and more settings like Non-IID.

- Writing: 
1. The writing needs thorough revision, and the current form read more like an experimental report.  
2. Please split the experiment and proposed framework.
3. Don't introduce too much basic knowledge in your proposed method.
4. Please give a formalized/mathematical description on your method, it is unlikely to understand the proposed method in detail from pure text descriptions.
5. Some statements are unclear. What is the k in 191?
Yes
n/a
No ethics review needed.
Please see the discussion on weaknesses above.
I cannot identify any strength of the paper.
The paper evaluates a label-flipping attack in federated learning and proposes a simple outlier detection approach using PCA and SVM to filter out compromised updates.
The paper has many typos. 

Both label-flipping attacks and outlier detection methods have been well-studied in federated learning. See, for example, "Defending against the Label-flipping Attack in Federated Learning" by Najeeb Moharram Jebreel et al. The paper does not seem to propose anything new. 

The FL setting and the threat model are poorly described and involve multiple unreasonable assumptions. What do you mean by training FL models without the adversarial settings? How can you ensure that there is no adversarial device during training? Also, what is the testing dataset D_test? Where does it come from, and why is it disjoint from the participants' datasets? In FL, the data is typically provided by participants. 

The evaluation considers a very small setting with three devices and iid data distribution. The results are from being convincing.
Yes
N/A
No ethics review needed.
+ What is the threat model assumed by the authors for the paper? What are the capabilities that the adversary has to compromise the system? 
+ How this paper advances the state of the art compared to other competing methods in the research literature on robust federated learning?
+ The paper proposes a defense against label flipping attacks in the context of applications for autonomous vehicles, which is a relevant challenge for the security and robustness of distributed learning techniques such as federated learning. 
+ The authors present a novel defense integrating PCA and MCSVM for detecting malicious label flipping attacks in federated learning, which effectively enhances the robustness of the algorithm during training against this type of attack.
The paper introduces a defense against label flipping attacks in federated learning in the context of applications for autonomous vehicles. The defense combines anomaly detection and robust aggregation to mitigate the effect of potential attacks by filtering out malicious model updates. The experimental evaluation against label flipping attacks in benchmark datasets show that the method is capable of mitigating the effect of the attack.
+ The novelty and the contribution of the method compared to existing defenses in the research literature is unclear. Many defenses are already capable of defending against the type of attacks explored in the paper. In this sense, the authors did not discuss these aspects in the paper and did not compare against any other competing method in the experiments. 
+ The settings for the experiments are relatively trivial. The authors just tested in an environment with IID data partitions and with a very reduced number of participants (just 3). As mentioned before, there is no comparison with other existing methods in the research literature. 
+ Although the paper aims to focus on autonomous vehicle applications, the paper really restricts to computer vision applications. It would be interesting to analyze more applications typical for autonomous vehicles (e.g., LiDAR, sensing, etc.). The way the paper is presented differs little from the majority of the papers in this area, which often consider similar computer vision benchmarks (e.g., CIFAR) for their experiments.
Yes
NA
No ethics review needed.
Comments:

- The novelty of this paper needs to be further strengthened. Currently, it appears to combine existing techniques—namely anomaly detection and robust aggregation—without a clear distinction from prior work. The authors should further elaborate on the differences and connections between their approach and existing methods to better demonstrate its unique contributions.

- The paper should include more advanced poisoning attack baselines. The experimental results do not compare against any state-of-the-art poisoning attack baselines, which limits the ability to highlight the superiority of the proposed defense mechanism.

- The paper also requires more advanced defense mechanism comparisons. To comprehensively evaluate performance, the proposed defense should be fairly compared with existing advanced defenses. Additionally, the authors should explain why their approach demonstrates superior performance relative to other defenses.

- Finally, the paper should incorporate more extensive experimental evaluations. The authors currently present only one set of experiments, which is insufficient for a convincing analysis. To provide a thorough evaluation, additional experiments are recommended.
Strengths:
+ This work offers a defense mechanism for FL against data poisoning attacks, specifically focusing on data-flipping attacks in AVs.
This paper presents a defense mechanism for FL against data poisoning attacks, specifically focusing on data-flipping attacks in AVs. The approach combines anomaly detection with robust aggregation techniques, using statistical outlier detection and model consistency checks to filter out compromised updates. Experimental results on benchmark datasets show that the method effectively improves FL robustness, preventing nearly a 15% accuracy drop in the global model and reducing the attack success rate, even at a 20% poisoning level. This solution enhances FL system security against adversarial threats in sensitive data-driven fields.
Weaknesses:
- The novelty of this paper needs to be further improved.
- The experimental results of this paper are not convincing.
- More poisoning attack baselines and advanced defenses need to be included.
- The writing quality of this paper needs another round of polishing.
This paper proposes a novel defense mechanism for Federated Learning systems, aimed at mitigating data poisoning attacks in autonomous vehicles. By combining anomaly detection with robust aggregation techniques.
@inproceedings{
anonymous2024robust,
title={Robust Federated Learning Frameworks Guarding Against Data Flipping Threats for Autonomous Vehicles},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=oA5GmyvMUY},
note={under review}
}
Federated Learning (FL) has become an established technique to facilitate privacy-preserving collaborative training across a multitude of clients. The ability to achieve collaborative learning from multiple parties containing an extensive volume of data while providing the essence of data privacy made it an attractive solution to address numerous challenges in sensitive data-driven fields such as autonomous vehicles (AVs). However, its decentralized nature exposes it to security threats, such as evasion and data poisoning attacks, where malicious participants can compromise training data. This paper addresses the challenge of defending federated learning systems against data poisoning attacks specifically focusing on data-flipping techniques in AVs by proposing a novel defense mechanism that combines anomaly detection with robust aggregation techniques. Our approach employs statistical outlier detection and model-based consistency checks to filter out compromised updates before they affect the global model. Experiments on benchmark datasets show that our method significantly enhances robustness by preventing nearly 15\% of accuracy drop for our global model when confronted with a malicious participant and reduction the the attack success rate even when dealing with 20\% of poisoning level. These findings provide a comprehensive solution to strengthen FL systems against adversarial threats.
I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
Federated Learning Data Poisoning Adversarial Attack
I certify that there is no acknowledgement section in this submission for double blind review.
/pdf/f6b3608a74513393e210cafedfd4b34fbb7e6cb0.pdf
alignment, fairness, safety, privacy, and societal considerations
I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6.
I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.
Robust Federated Learning Frameworks Guarding Against Data Flipping Threats for Autonomous Vehicles
ICLR 2025 Conference Submission
ICLR.cc/2025/Conference/Submission