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Demystify Federated Learning

Date: Tues, 26, April

Time: 19:00-20:00 HKT

Venue: Zoom Event

(Meeting ID: 830 3487 7589 | Passcode: 738004)

About this event

AI works best with learning from sufficiently large curated data. However, there are concerns on user data privacy and data protection with existing machine-learning approaches of data centralization. Federated learning (FL)is a new branch in AI that has opened the door for a new era of machine learning.

FL is an emerging machine learning paradigm that enables multi party collaborative model training with the collaborated parties’ data resided locally – decentralised data. This new machine learning paradigm has the merit of no data sharing and thereby reducing privacy leakage while breaking data silos. Thus, it is able to provide a more personalized experience without compromising on user privacy.

Guest Speaker

Isaac Wong (Webank Principal AI solution architect) will breakdown the unique concept in Federated Learning like (Homomorphic encryption, Model aggregation) for the audience. He will use a real-life example to demonstrate how companies maximize the benefits of FL.