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IEEE/ICACT20210192 Question.5 Questioner: joomi@inje.ac.kr 2021-02-09 오후 12:59:02	IEEE/ICACT20210192 Answer.5 Answer by Auhor satyabrataaich@gmail.com 2021-02-09 오후 12:59:02
Thank you for your slides. I would like to know how FL is protecting the privacy of healthcare record compared to the traditional method.	Thank you for the question.Traditional machine learning involves a data pipeline that uses a central server that hosts the trained model in order to make predictions.The downside of this architecture is that all the data collected by local devices and sensors are sent back to the central server for processing, and subsequently returned back to the devices. This round-trip limits a model’s ability to learn in real-time.Federated learning (FL) in contrast, is an approach that downloads the current model and computes an updated model at the device itself (ala edge computing) using local data.In a more general sense, Federated Learning allows for machine learning algorithms to gain experience from a broad range of data sets located at different locations. The approach enables multiple organizations to collaborate on the development of models, but without needing to directly share secure data with each other

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