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Ref. No | Technologies used | Key contributions | Limitations |
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[56] | Differential privacy techniques | Using the BraTS dataset, assess the usefulness of practical FL methods for segmenting brain tumors | It is impossible to collect and share patient data in a centralized data lake |
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[58] | CFL-based collaborative learning framework | To highlight the potential of intelligent processing of clinical data at the edge, open research issues related to deploying ML at the edge for healthcare applications that re- quire further investigation | (i) Image size and quality |
(ii) Contrast and brightness level, and |
(iii) Positioning of subjects |
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[59] | Skin imaging technology | The proposed model contains two core contributions: | Health practitioners usually apply manual or computer vision-based tools for skin tumor diagnosis, which may cause misinterpretation of the disease and lead to a longer analysis time |
(i) The model was deployed on the cloud server, and |
(ii) Its deployment on the edges majorly contributes toward adaptability by continuously updating |
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[60] | FL techniques | The performance of FL may be enhanced with more images or data augmentation | Comparisons of FL with unequal data distribution, data augmentation, and one-shot learning are required to explore the implications of data imbalance |
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[62] | 3D-convolutional neural network technique | FL study on cardiovascular magnetic resonance diagnosis and demonstrate that FL performance is comparable to central database server | Patient privacy |
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