Towards Attack-tolerant Federated Learning via Critical Parameter Analysis

• URL: http://arxiv.org/abs/2308.09318v1
• Date: Fri, 18 Aug 2023 05:37:55 GMT
• Title: Towards Attack-tolerant Federated Learning via Critical Parameter Analysis
• Authors: Sungwon Han, Sungwon Park, Fangzhao Wu, Sundong Kim, Bin Zhu, Xing Xie and Meeyoung Cha
• Abstract summary: Federated learning systems are susceptible to poisoning attacks when malicious clients send false updates to the central server. This paper proposes a new defense strategy, FedCPA (Federated learning with Critical Analysis) Our attack-tolerant aggregation method is based on the observation that benign local models have similar sets of top-k and bottom-k critical parameters, whereas poisoned local models do not.
• Score: 85.41873993551332
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning is used to train a shared model in a decentralized way without clients sharing private data with each other. Federated learning systems are susceptible to poisoning attacks when malicious clients send false updates to the central server. Existing defense strategies are ineffective under non-IID data settings. This paper proposes a new defense strategy, FedCPA (Federated learning with Critical Parameter Analysis). Our attack-tolerant aggregation method is based on the observation that benign local models have similar sets of top-k and bottom-k critical parameters, whereas poisoned local models do not. Experiments with different attack scenarios on multiple datasets demonstrate that our model outperforms existing defense strategies in defending against poisoning attacks.

Related papers

• Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated Learning [12.352511156767338]
  Federated learning is highly susceptible to model poisoning attacks. In this paper, we propose AdaAggRL, an RL-based Adaptive aggregation method. Experiments on four real-world datasets demonstrate that the proposed defense model significantly outperforms widely adopted defense models for sophisticated attacks.
  arXiv  Detail & Related papers  (2024-06-20T11:33:14Z)
• Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks [48.70867241987739]
  InferGuard is a novel Byzantine-robust aggregation rule aimed at defending against client-side training data distribution inference attacks. The results of our experiments indicate that our defense mechanism is highly effective in protecting against client-side training data distribution inference attacks.
  arXiv  Detail & Related papers  (2024-03-05T17:41:35Z)
• FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning [98.43475653490219]
  Federated learning (FL) is susceptible to poisoning attacks. FreqFed is a novel aggregation mechanism that transforms the model updates into the frequency domain. We demonstrate that FreqFed can mitigate poisoning attacks effectively with a negligible impact on the utility of the aggregated model.
  arXiv  Detail & Related papers  (2023-12-07T16:56:24Z)
• FedDefender: Client-Side Attack-Tolerant Federated Learning [60.576073964874]
  Federated learning enables learning from decentralized data sources without compromising privacy. It is vulnerable to model poisoning attacks, where malicious clients interfere with the training process. We propose a new defense mechanism that focuses on the client-side, called FedDefender, to help benign clients train robust local models.
  arXiv  Detail & Related papers  (2023-07-18T08:00:41Z)
• Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations [55.2480439325792]
  Federated Learning (FL) facilitates decentralized machine learning model training, preserving data privacy, lowering communication costs, and boosting model performance through diversified data sources. FL faces vulnerabilities such as poisoning attacks, undermining model integrity with both untargeted performance degradation and targeted backdoor attacks. We define a new notion of strong adaptive adversaries, capable of adapting to multiple objectives simultaneously. MESAS is the first defense robust against strong adaptive adversaries, effective in real-world data scenarios, with an average overhead of just 24.37 seconds.
  arXiv  Detail & Related papers  (2023-06-06T11:44:42Z)
• Client-specific Property Inference against Secure Aggregation in Federated Learning [52.8564467292226]
  Federated learning has become a widely used paradigm for collaboratively training a common model among different participants. Many attacks have shown that it is still possible to infer sensitive information such as membership, property, or outright reconstruction of participant data. We show that simple linear models can effectively capture client-specific properties only from the aggregated model updates.
  arXiv  Detail & Related papers  (2023-03-07T14:11:01Z)
• FedCC: Robust Federated Learning against Model Poisoning Attacks [0.0]
  Federated Learning is designed to address privacy concerns in learning models. New distributed paradigm safeguards data privacy but differentiates the attack surface due to the server's inaccessibility to local datasets.
  arXiv  Detail & Related papers  (2022-12-05T01:52:32Z)
• Defending against the Label-flipping Attack in Federated Learning [5.769445676575767]
  Federated learning (FL) provides autonomy and privacy by design to participating peers. The label-flipping (LF) attack is a targeted poisoning attack where the attackers poison their training data by flipping the labels of some examples. We propose a novel defense that first dynamically extracts those gradients from the peers' local updates.
  arXiv  Detail & Related papers  (2022-07-05T12:02:54Z)
• A Framework for Evaluating Gradient Leakage Attacks in Federated Learning [14.134217287912008]
  Federated learning (FL) is an emerging distributed machine learning framework for collaborative model training with a network of clients. Recent studies have shown that even sharing local parameter updates from a client to the federated server may be susceptible to gradient leakage attacks. We present a principled framework for evaluating and comparing different forms of client privacy leakage attacks.
  arXiv  Detail & Related papers  (2020-04-22T05:15:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.