Feature-Based Dataset Fingerprinting for Clustered Federated Learning on Medical Image Data
Federated Learning (FL) allows multiple clients to train a common model without sharing their private training data. In practice, federated optimization struggles with sub-optimal model utility because data is not independent and identically distributed (non-IID). Recent work has proposed to cluster...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2024.2394756 |
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| author | Daniel Scheliga Patrick Mäder Marco Seeland |
| author_facet | Daniel Scheliga Patrick Mäder Marco Seeland |
| author_sort | Daniel Scheliga |
| collection | DOAJ |
| description | Federated Learning (FL) allows multiple clients to train a common model without sharing their private training data. In practice, federated optimization struggles with sub-optimal model utility because data is not independent and identically distributed (non-IID). Recent work has proposed to cluster clients according to dataset fingerprints to improve model utility in such situations. These fingerprints aim to capture the key characteristics of clients’ local data distributions. Recently, a mechanism was proposed to calculate dataset fingerprints from raw client data. We find that this fingerprinting mechanism comes with substantial time and memory consumption, limiting its practical use to small datasets. Additionally, shared raw data fingerprints can directly leak sensitive visual information, in certain cases even resembling the original client training data. To alleviate these problems, we propose a Feature-based dataset FingerPrinting mechanism (FFP). We use the MedMNIST database to develop a highly realistic case study for FL on medical image data. Compared to existing methods, our proposed FFP reduces the computational overhead of fingerprint calculation while achieving similar model utility. Furthermore, FFP mitigates the risk of raw data leakage from fingerprints by design. |
| format | Article |
| id | doaj-art-09c8d0aa6936474bb77dbd525abbaac5 |
| institution | OA Journals |
| issn | 0883-9514 1087-6545 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj-art-09c8d0aa6936474bb77dbd525abbaac52025-08-20T02:36:22ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452024-12-0138110.1080/08839514.2024.2394756Feature-Based Dataset Fingerprinting for Clustered Federated Learning on Medical Image DataDaniel Scheliga0Patrick Mäder1Marco Seeland2Department of Computer Science and Automation, Data-intensive Systems and Visualization Group (dAI.SY), Technische Universität Ilmenau, Ilmenau, GermanyDepartment of Computer Science and Automation, Data-intensive Systems and Visualization Group (dAI.SY), Technische Universität Ilmenau, Ilmenau, GermanyDepartment of Computer Science and Automation, Data-intensive Systems and Visualization Group (dAI.SY), Technische Universität Ilmenau, Ilmenau, GermanyFederated Learning (FL) allows multiple clients to train a common model without sharing their private training data. In practice, federated optimization struggles with sub-optimal model utility because data is not independent and identically distributed (non-IID). Recent work has proposed to cluster clients according to dataset fingerprints to improve model utility in such situations. These fingerprints aim to capture the key characteristics of clients’ local data distributions. Recently, a mechanism was proposed to calculate dataset fingerprints from raw client data. We find that this fingerprinting mechanism comes with substantial time and memory consumption, limiting its practical use to small datasets. Additionally, shared raw data fingerprints can directly leak sensitive visual information, in certain cases even resembling the original client training data. To alleviate these problems, we propose a Feature-based dataset FingerPrinting mechanism (FFP). We use the MedMNIST database to develop a highly realistic case study for FL on medical image data. Compared to existing methods, our proposed FFP reduces the computational overhead of fingerprint calculation while achieving similar model utility. Furthermore, FFP mitigates the risk of raw data leakage from fingerprints by design.https://www.tandfonline.com/doi/10.1080/08839514.2024.2394756 |
| spellingShingle | Daniel Scheliga Patrick Mäder Marco Seeland Feature-Based Dataset Fingerprinting for Clustered Federated Learning on Medical Image Data Applied Artificial Intelligence |
| title | Feature-Based Dataset Fingerprinting for Clustered Federated Learning on Medical Image Data |
| title_full | Feature-Based Dataset Fingerprinting for Clustered Federated Learning on Medical Image Data |
| title_fullStr | Feature-Based Dataset Fingerprinting for Clustered Federated Learning on Medical Image Data |
| title_full_unstemmed | Feature-Based Dataset Fingerprinting for Clustered Federated Learning on Medical Image Data |
| title_short | Feature-Based Dataset Fingerprinting for Clustered Federated Learning on Medical Image Data |
| title_sort | feature based dataset fingerprinting for clustered federated learning on medical image data |
| url | https://www.tandfonline.com/doi/10.1080/08839514.2024.2394756 |
| work_keys_str_mv | AT danielscheliga featurebaseddatasetfingerprintingforclusteredfederatedlearningonmedicalimagedata AT patrickmader featurebaseddatasetfingerprintingforclusteredfederatedlearningonmedicalimagedata AT marcoseeland featurebaseddatasetfingerprintingforclusteredfederatedlearningonmedicalimagedata |