www.icact.org

Member Login

TACT Journal Homepage

Call for Paper

Committee

Find My Paper

Author Homepage

Statistics & History

Welcome Message

Paper Procedure

FAQ

Paper Archives

Outstanding Papers

Program & Proceedings

Presentation Platform

Hotel & Travel Info

Photo Gallery

Scheduler Login

Seminar

Archives Login

Sponsors

Virual Oral Question and Answer


IEEE/ICACT20210192 Question.4 Questioner: James_Lan@htx.gov.sg 2021-02-08 오후 4:49:25	IEEE/ICACT20210192 Answer.4 Answer by Auhor satyabrataaich@gmail.com 2021-02-08 오후 4:49:25
Hi, thanks for the sharing. Can share your views on how does privacy regulation affects Federated Learning?	Thank you so much for the question. It's a great 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. We can say that Federated learning helps to develop robust model without revealing private information of the user.

Select Voice