Federated Learning: Practice and Modern Algorithms
Federated Learning: Practice and Modern Algorithms


Federated Learning is an emerging paradigm that enables one to perform machine learning without centralizing training data in a single place, allowing local clients to collaboratively train a shared global model.
Federated learning offers a solution for consortia, multi-national enterprises, and networks of edge devices to benefit from training across individual datasets while respecting data privacy concerns, and accommodating network bandwidth limitations and limited device availability.

In this workshop you will learn when and why federated learning should be used, basic algorithms for implementing it, as well as more advanced ones covering a variety of use-cases. Towards the end of the workshop participants will be offered a hands-on experience of training a federated model together.


Chaitanya Kumar is a Machine Learning Engineer with the Real World AI team in the IBM Research Singapore Lab, where his work focuses on Federated Learning. Owing to his background in distributed systems, he contributes to the orchestration and deployment of Federated Learning pipelines.

Open Data Science




Open Data Science
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Cambridge, MA 02142

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