Collaboration in health is heavily impeded by lack of trust, data sharing regulations and limited consent of patients. In settings where different institutions hold different modalities of patient data in the form of electronic health records (EHR), picture archiving and communication systems (PACS) for radiology and other imaging data, pathology test results, or other sensitive data such as genetic markers for disease, collaborative training of distributed machine learning models without any data sharing or leakage of patterns about raw data is desired. In addition the solution needs to be resource efficient in terms of communication bandwidth, computations and memory. This talk is primarily about a recently developed, highly resource efficient method called 'Split Learning' for this very purpose by allowing to perform distributed deep learning under these constraints.
Praneeth Vepakomma is currently a grad student and researcher at MIT in Camera Culture research group where his focus is on developing algorithms to support distributed and collaborative machine learning. He was previously a scientist at Amazon, Motorola Solutions and at various startups all of which were successfully acquired.
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