Train ML Model Without Sharing Your Data! (Federated Learning on Azure)

In this hands-on video , we will explore how to use federated learning with Azure ML to train machine learning models on distributed data. Federated learning is a technique that allows multiple parties to collaborate on a model training process without sharing their data directly. Instead, each party trains the model on their local data and only shares model updates with a central server. This approach can help to address privacy concerns and data ownership issues that arise when training models on sensitive or decentralized data.

Referenced GitHub repo in the video:

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#FederatedLearning #AzureML #DistributedData #Privacy #Security #MachineLearning #DataScience #Collaboration #DecentralizedData #ModelTraining #Encryption #Authentication #AccessControl #DataOwnership #ModelDeployment #Tutorial #AI #ML

[cer_oz]

Hi MG, thanks for the great tutorials. Although i haven't watched this particular video yet, i want to give an advice about your youtube channel which will be very useful for me and also for other people. firstly you are doing a great job, but your channel is a bit of a mess. Please make some nice and more general topic based (rather than very specific) playlists. most of your playlist consists of a single video. i hope i can express myself enough. Good luck with your journey.