Browsing by Subject "Federated learning"

Now showing 1 - 5 of 5
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    Defense Strategies Toward Model Poisoning Attacks in Federated Learning: A Survey
    (IEEE, 2022-04) Wang, Zhilin; Kang, Qiao; Zhang, Xinyi; Hu, Qin; Computer and Information Science, School of Science
    Advances in distributed machine learning can empower future communications and networking. The emergence of federated learning (FL) has provided an efficient framework for distributed machine learning, which, however, still faces many security challenges. Among them, model poisoning attacks have a significant impact on the security and performance of FL. Given that there have been many studies focusing on defending against model poisoning attacks, it is necessary to survey the existing work and provide insights to inspire future research. In this paper, we first classify defense mechanisms for model poisoning attacks into two categories: evaluation methods for local model updates and aggregation methods for the global model. Then, we analyze some of the existing defense strategies in detail. We also discuss some potential challenges and future research directions. To the best of our knowledge, we are the first to survey defense methods for model poisoning attacks in FL.
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    Evaluation of federated learning variations for COVID-19 diagnosis using chest radiographs from 42 US and European hospitals
    (Oxford University Press, 2022) Peng, Le; Luo, Gaoxiang; Walker, Andrew; Zaiman, Zachary; Jones, Emma K.; Gupta, Hemant; Kersten, Kristopher; Burns, John L.; Harle, Christopher A.; Magoc, Tanja; Shickel, Benjamin; Steenburg, Scott D.; Loftus, Tyler; Melton, Genevieve B.; Wawira Gichoya, Judy; Sun, Ju; Tignanelli, Christopher J.; Radiology and Imaging Sciences, School of Medicine
    Objective: Federated learning (FL) allows multiple distributed data holders to collaboratively learn a shared model without data sharing. However, individual health system data are heterogeneous. "Personalized" FL variations have been developed to counter data heterogeneity, but few have been evaluated using real-world healthcare data. The purpose of this study is to investigate the performance of a single-site versus a 3-client federated model using a previously described Coronavirus Disease 19 (COVID-19) diagnostic model. Additionally, to investigate the effect of system heterogeneity, we evaluate the performance of 4 FL variations. Materials and methods: We leverage a FL healthcare collaborative including data from 5 international healthcare systems (US and Europe) encompassing 42 hospitals. We implemented a COVID-19 computer vision diagnosis system using the Federated Averaging (FedAvg) algorithm implemented on Clara Train SDK 4.0. To study the effect of data heterogeneity, training data was pooled from 3 systems locally and federation was simulated. We compared a centralized/pooled model, versus FedAvg, and 3 personalized FL variations (FedProx, FedBN, and FedAMP). Results: We observed comparable model performance with respect to internal validation (local model: AUROC 0.94 vs FedAvg: 0.95, P = .5) and improved model generalizability with the FedAvg model (P < .05). When investigating the effects of model heterogeneity, we observed poor performance with FedAvg on internal validation as compared to personalized FL algorithms. FedAvg did have improved generalizability compared to personalized FL algorithms. On average, FedBN had the best rank performance on internal and external validation. Conclusion: FedAvg can significantly improve the generalization of the model compared to other personalization FL algorithms; however, at the cost of poor internal validity. Personalized FL may offer an opportunity to develop both internal and externally validated algorithms.
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    Federated learning as a catalyst for digital healthcare innovations
    (Elsevier, 2024-07-12) Yang, Guang; Edwards, Brandon; Bakas, Spyridon; Dou, Qi; Xu, Daguang; Li, Xiaoxiao; Wang, Wanying; Pathology and Laboratory Medicine, School of Medicine
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    Privacy preservation for federated learning in health care
    (Elsevier, 2024-07-12) Pati, Sarthak; Kumar, Sourav; Varma, Amokh; Edwards, Brandon; Lu, Charles; Qu, Liangqiong; Wang, Justin J.; Lakshminarayanan, Anantharaman; Wang, Shih-han; Sheller, Micah J.; Chang, Ken; Singh, Praveer; Rubin, Daniel L.; Kalpathy-Cramer, Jayashree; Bakas, Spyridon; Pathology and Laboratory Medicine, School of Medicine
    Artificial intelligence (AI) shows potential to improve health care by leveraging data to build models that can inform clinical workflows. However, access to large quantities of diverse data is needed to develop robust generalizable models. Data sharing across institutions is not always feasible due to legal, security, and privacy concerns. Federated learning (FL) allows for multi-institutional training of AI models, obviating data sharing, albeit with different security and privacy concerns. Specifically, insights exchanged during FL can leak information about institutional data. In addition, FL can introduce issues when there is limited trust among the entities performing the compute. With the growing adoption of FL in health care, it is imperative to elucidate the potential risks. We thus summarize privacy-preserving FL literature in this work with special regard to health care. We draw attention to threats and review mitigation approaches. We anticipate this review to become a health-care researcher's guide to security and privacy in FL.
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    Social Welfare Maximization in Cross-Silo Federated Learning
    (IEEE, 2022-05-23) Chen, Jianan; Hu, Qin; Jiang, Honglu; Computer and Information Science, School of Science
    As one of the typical settings of Federated Learning (FL), cross-silo FL allows organizations to jointly train an optimal Machine Learning (ML) model. In this case, some organizations may try to obtain the global model without contributing their local training, lowering the social welfare. In this paper, we model the interactions among organizations in cross-silo FL as a public goods game for the first time and theoretically prove that there exists a social dilemma where the maximum social welfare is not achieved in Nash equilibrium. To over-come this social dilemma, we employ the Multi-player Multi-action Zero-Determinant (MMZD) strategy to maximize the social welfare. With the help of the MMZD, an individual organization can unilaterally control the social welfare without extra cost. Experimental results validate that the MMZD strategy is effective in maximizing the social welfare.