Multi-limb Split Learning for Tumor Classification on Vertically Distributed Data
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Abstract (arXiv)
Brain tumors are one of the life-threatening forms of cancer. Previous studies have classified brain tumors using deep neural networks. In this paper, we perform the later task using a collaborative deep learning technique, more specifically split learning. Split learning allows collaborative learning via neural networks splitting into two (or more) parts, a client-side network and a server-side network. The client-side is trained to a certain layer called the cut layer. Then, the rest of the training is resumed on the server-side network. Vertical distribution, a method for distributing data among organizations, was implemented where several hospitals hold different attributes of information for the same set of patients. To the best of our knowledge this paper will be the first paper to implement both split learning and vertical distribution for brain tumor classification. Using both techniques, we were able to achieve train and test accuracy greater than 90% and 70%, respectively.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 13 of its 26 references that are in the map. First posted on arXiv 27 Jan 2023.
Builds on
13 of 26 references.
References · 13
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- 2018Activation Functions: Comparison of trends in Practice and Research for Deep Learningcited by 130
- 2019Split Learning for collaborative deep learning in healthcarecited by 72
- 2020SplitNN-driven Vertical Partitioningcited by 42
- 2020Distributed Training of Deep Learning Models: A Taxonomic Perspectivecited by 25
- 2020Privacy Preservation in Federated Learning: An insightful survey from the GDPR Perspectivecited by 7
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2 papers.