Review on security of federated learning and its application in healthcare

Li, Hao; Li, Chengcheng; Wang, Jian; Yang, Aimin; Ma, Zezhong; Zhang, Zunqian; Hua, Dianbo

doi:10.1016/j.future.2023.02.021

Cited by 43 publications

(18 citation statements)

References 81 publications

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“…FL has not fully addressed the privacy concerns associated with data sharing. 27,28 Various attack strategies, such as model inversion attacks, can still infer sensitive information about patients by accessing the shared weights between clients and the server. Thus, more sophisticated privacy-preserving approaches, such as secure multiparty computation, differential privacy, and homomorphic encryption, have been combined with FL to further protect the shared information.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, more sophisticated privacy-preserving approaches, such as secure multiparty computation, differential privacy, and homomorphic encryption, have been combined with FL to further protect the shared information. 27 Although these approaches improve the obtained privacy, they negatively impact the performance, 28 and a trade-off between privacy and performance has to be considered in practice.…”

Section: Discussionmentioning

confidence: 99%

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Federated learning for prostate cancer detection in biparametric MRI: optimization of rounds, epochs, and aggregation strategy

Moradi,

Zerka,

Sander Bosma

et al. 2024

Medical Imaging 2024: Computer-Aided Diagnosis

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In 2020, prostate cancer (PCa) caused about 6.8% of male cancer-related deaths. Thus, early detection of PCa and improving clinical procedures for suspected cases are crucial. Fortunately, using biparametric magnetic resonance imaging (bpMRI) along with advanced computer-aided diagnosis (CAD) systems that involve deep learning (DL) techniques leads to PCa detection rates comparable to radiologists. Achieving this requires access to a large amount of patient data; however, sharing patient-specific information raises privacy concerns. To address this, we present a Flower federated learning (FL) framework that utilizes local DL models based on nnU-Net for PCa detection. We investigated the impact of various epoch-round combinations and server-side aggregation strategies on the performance of the FL framework and compared the results with centralized learning (CL). We performed empirical experiments using the Prostate Imaging Cancer AI (PI-CAI) dataset comprising 1500 patients from three different institutions for training/validation/testing and further evaluated the results on an independent test set of 199 patients from a fourth institution. The trained models were evaluated based on the PI-CAI score, which combines patient-level diagnostic performance (area under the receiver operating characteristic) and lesion-level detection performance (average precision). We found that FL can replicate the performance of CL and substantially improve the detection of PCa compared to local training approaches. We optimized the combination of epochs and rounds, along with a server-side aggregation strategy, for prostate lesion detection on the validation set. This configuration (5 epoch, 200 rounds, FedAdagrad aggregation) led to an enhanced PI-CAI score of 0.74 on the test set, compared to 0.73 and 0.72 for the CL and FL baselines, respectively.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Federated learning for prostate cancer detection in biparametric MRI: optimization of rounds, epochs, and aggregation strategy

Moradi,

Zerka,

Sander Bosma

et al. 2024

Medical Imaging 2024: Computer-Aided Diagnosis

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“…Generative selfsupervised learning tries to encode the input object and set learning tasks between different parts or perspectives to predict or generate the output, so as to understand the intrinsic properties and semantics of the object [14,15]. Contrastive Learning (CL) [11,[16][17][18][19], as a typical discriminative learning, distinguishes similar and dissimilar data points by comparing the representation of learning data, and learns the representation of data to capture the basic structure and relationship between different data points, without requiring the ability to re-generate the original image. Therefore, the task is less difficult than that of generative self-supervised learning.…”

Section: Self-supervised Learning Framework In Image Classificationmentioning

confidence: 99%

A Thangka cultural element classification model based on self-supervised contrastive learning and MS Triplet Attention

Tang,

Xie

2024

Vis Comput

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Being a significant repository of Buddhist imagery, Thangka images are valuable historical materials of Tibetan studies, which covers many domains such as Tibetan history, politics, culture, social life and even traditional medicine and astronomy. Thangka cultural element images are the essence of Thangka images. Hence Thangka cultural element images classification is one of the most important work of knowledge representation and mining in the field of Thangka, and is the foundation of digital protection of Thangka images. However, due to the limited quantity, high complexity and the intricate textures of Thangka images, the classification of Thangka images is limited to a small number of categories and coarse granularity. Thus a novel fusion texture feature dual-branch Thangka cultural elements classification model based on the attention mechanism and selfsupervised contrastive learning has been proposed in this paper. Specifically, to address the issue of insufficient labeled samples and improve the classification performance, this method utilizes a large amount of unlabeled irrelevant data to pre-train the feature extractor through self-supervised learning. During the fine-tuning stage of the downstream task, a dual-branch feature extraction structure incorporating texture features has been designed, and MS-Triplet Attetnion proposed by us is used for the integration of important features. Additionally, to address the problem of sample imbalance and the existence of a large number of difficult samples in the Thangka cultural element data set, the Gradient Harmonizing Mechanism Loss has been adopted, and it has been improved by introducing a self designed adaptive mechanism. The experimental results on Thangka cultural elements dataset prove the superiority of the proposed method over the state-of-the-art methods.The source code of our proposed algorithm and the related datasets is available at https://github.com/WiniTang/MS-BiCLR.

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“…This paradigm has been suggested to be deployed in several fields, including IoT applications [2][3][4], industry applications [5], network applications [6] and so on. While FL offers significant advantages in distributed environments, it concurrently faces substantial security challenges [7,8], particularly from malicious clients. Among these, the poisoning attack is a critical threat.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Robustness within the Collaborative Federated Learning Framework: A Novel Grouping Algorithm for Edge Clients

Su,

Liu,

et al. 2024

Applied Sciences

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In this study, we introduce a novel collaborative federated learning (FL) framework, aiming at enhancing robustness in distributed learning environments, particularly pertinent to IoT and industrial automation scenarios. At the core of our contribution is the development of an innovative grouping algorithm for edge clients. This algorithm employs a distinctive ID distribution function, enabling efficient and secure grouping of both normal and potentially malicious clients. Our proposed grouping scheme accurately determines the numerical difference between normal and malicious groups under various network scenarios. Our method addresses the challenge of model poisoning attacks, ensuring the accuracy of outcomes in a collaborative federated learning framework. Our numerical experiments demonstrate that our grouping scheme effectively limits the number of malicious groups. Additionally, our collaborative FL framework has shown resilience against various levels of poisoning attack abilities and maintained high prediction accuracy across a range of scenarios, showcasing its robustness against poisoning attacks.

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Review on security of federated learning and its application in healthcare

Cited by 43 publications

References 81 publications

Federated learning for prostate cancer detection in biparametric MRI: optimization of rounds, epochs, and aggregation strategy

Federated learning for prostate cancer detection in biparametric MRI: optimization of rounds, epochs, and aggregation strategy

A Thangka cultural element classification model based on self-supervised contrastive learning and MS Triplet Attention

Enhancing Robustness within the Collaborative Federated Learning Framework: A Novel Grouping Algorithm for Edge Clients

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