tinyML Talks: Unsupervised Federated Learning

Modern day applications such as autonomous vehicles, IoT, smart grids, etc., generate massive amounts of data at the edge. Federated Learning (FL) enables machine learning without having to transfer data from edge devices to any untrusted third party. A fundamental challenge in federated supervised learning is ensuring that data at the edge is annotated. This talk gives a general overview of federated learning with specific focuss on federated learning techniques that utilise the unannotated data at the edge for learning a global model.


September 14, 2023



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Timezone: PDT

Unsupervised Federated Learning

Ranjitha PRASAD, Assistant Professor

IIIT Delhi

Ranjitha PRASAD, Assistant Professor

IIIT Delhi

Dr. Ranjitha Prasad obtained her Ph.D. from Indian Institute of Science in 2015. Her experience is in the general areas of signal processing, Bayesian statistics, and more recently, machine learning and deep neural networks. She has been a postdoctoral researcher at Nanyang Technological University and National University of Singapore, Singapore, and a scientist at TCS Innovation Labs, Delhi. Her current research interests are Causal Inference, explainable AI and federated learning.

Schedule subject to change without notice.