Suppressing Poisoning Attacks on Federated Learning for Medical Imaging

• URL: http://arxiv.org/abs/2207.10804v1
• Date: Fri, 15 Jul 2022 00:43:34 GMT
• Title: Suppressing Poisoning Attacks on Federated Learning for Medical Imaging
• Authors: Naif Alkhunaizi, Dmitry Kamzolov, Martin Tak\'a\v{c}, Karthik Nandakumar
• Abstract summary: We propose a robust aggregation rule called Distance-based Outlier Suppression (DOS) that is resilient to byzantine failures. The proposed method computes the distance between local parameter updates of different clients and obtains an outlier score for each client. The resulting outlier scores are converted into normalized weights using a softmax function, and a weighted average of the local parameters is used for updating the global model.
• Score: 4.433842217026879
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Collaboration among multiple data-owning entities (e.g., hospitals) can accelerate the training process and yield better machine learning models due to the availability and diversity of data. However, privacy concerns make it challenging to exchange data while preserving confidentiality. Federated Learning (FL) is a promising solution that enables collaborative training through exchange of model parameters instead of raw data. However, most existing FL solutions work under the assumption that participating clients are \emph{honest} and thus can fail against poisoning attacks from malicious parties, whose goal is to deteriorate the global model performance. In this work, we propose a robust aggregation rule called Distance-based Outlier Suppression (DOS) that is resilient to byzantine failures. The proposed method computes the distance between local parameter updates of different clients and obtains an outlier score for each client using Copula-based Outlier Detection (COPOD). The resulting outlier scores are converted into normalized weights using a softmax function, and a weighted average of the local parameters is used for updating the global model. DOS aggregation can effectively suppress parameter updates from malicious clients without the need for any hyperparameter selection, even when the data distributions are heterogeneous. Evaluation on two medical imaging datasets (CheXpert and HAM10000) demonstrates the higher robustness of DOS method against a variety of poisoning attacks in comparison to other state-of-the-art methods. The code can be found here https://github.com/Naiftt/SPAFD.

Related papers

• Federated Face Forgery Detection Learning with Personalized Representation [63.90408023506508]
  Deep generator technology can produce high-quality fake videos that are indistinguishable, posing a serious social threat. Traditional forgery detection methods directly centralized training on data. The paper proposes a novel federated face forgery detection learning with personalized representation.
  arXiv  Detail & Related papers  (2024-06-17T02:20:30Z)
• Federated Learning under Partially Class-Disjoint Data via Manifold Reshaping [64.58402571292723]
  We propose a manifold reshaping approach called FedMR to calibrate the feature space of local training. We conduct extensive experiments on a range of datasets to demonstrate that our FedMR achieves much higher accuracy and better communication efficiency.
  arXiv  Detail & Related papers  (2024-05-29T10:56:13Z)
• Federated Causal Discovery from Heterogeneous Data [70.31070224690399]
  We propose a novel FCD method attempting to accommodate arbitrary causal models and heterogeneous data. These approaches involve constructing summary statistics as a proxy of the raw data to protect data privacy. We conduct extensive experiments on synthetic and real datasets to show the efficacy of our method.
  arXiv  Detail & Related papers  (2024-02-20T18:53:53Z)
• Federated Meta-Learning for Few-Shot Fault Diagnosis with Representation Encoding [21.76802204235636]
  We propose representation encoding-based federated meta-learning (REFML) for few-shot fault diagnosis. REFML harnesses the inherent generalization among training clients, effectively transforming it into an advantage for out-of-distribution. It achieves an increase in accuracy by 2.17%-6.50% when tested on unseen working conditions of the same equipment type and 13.44%-18.33% when tested on totally unseen equipment types.
  arXiv  Detail & Related papers  (2023-10-13T10:48:28Z)
• Personalized Federated Learning under Mixture of Distributions [98.25444470990107]
  We propose a novel approach to Personalized Federated Learning (PFL), which utilizes Gaussian mixture models (GMM) to fit the input data distributions across diverse clients. FedGMM possesses an additional advantage of adapting to new clients with minimal overhead, and it also enables uncertainty quantification. Empirical evaluations on synthetic and benchmark datasets demonstrate the superior performance of our method in both PFL classification and novel sample detection.
  arXiv  Detail & Related papers  (2023-05-01T20:04:46Z)
• 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)
• Optimizing Server-side Aggregation For Robust Federated Learning via Subspace Training [80.03567604524268]
  Non-IID data distribution across clients and poisoning attacks are two main challenges in real-world federated learning systems. We propose SmartFL, a generic approach that optimize the server-side aggregation process. We provide theoretical analyses of the convergence and generalization capacity for SmartFL.
  arXiv  Detail & Related papers  (2022-11-10T13:20:56Z)
• FedPerm: Private and Robust Federated Learning by Parameter Permutation [2.406359246841227]
  Federated Learning (FL) is a distributed learning paradigm that enables mutually untrusting clients to collaboratively train a common machine learning model. Client data privacy is paramount in FL. At the same time, the model must be protected from poisoning attacks from adversarial clients. We present FedPerm, a new FL algorithm that addresses both these problems by combining a novel intra-model parameter shuffling technique that amplifies data privacy, with Private Information Retrieval (PIR) based techniques that permit cryptographic aggregation of clients' model updates.
  arXiv  Detail & Related papers  (2022-08-16T19:40:28Z)
• FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning [4.02923738318937]
  Uneven distribution of local data across different edge devices (clients) results in slow model training and accuracy reduction in federated learning. This work introduces a novel non-IID type encountered in real-world datasets, namely cluster-skew. We propose FedDRL, a novel FL model that employs deep reinforcement learning to adaptively determine each client's impact factor.
  arXiv  Detail & Related papers  (2022-08-04T04:24:16Z)
• BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning [0.0]
  We present BEAS, the first blockchain-based framework for N-party Federated Learning. It provides strict privacy guarantees of training data using gradient pruning. Anomaly detection protocols are used to minimize the risk of data-poisoning attacks. We also define a novel protocol to prevent premature convergence in heterogeneous learning environments.
  arXiv  Detail & Related papers  (2022-02-06T17:11:14Z)

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.