September 19-21, 2023
Bilbao, Spain
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Tuesday, September 19 • 12:20 - 13:00
FedLess: Secure and Scalable Serverless Federated Learning - Mohak Chadha, Technical University of Munich

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Federated learning (FL) is a machine learning paradigm that enables the collaborative training of a shared global model across distributed clients while keeping the training data local. However, conventional FL systems face several challenges such as limited scalability, complex infrastructure management, and wasted compute and incurred costs due to idling clients. These challenges of FL systems closely align with the several advantages that an emerging cloud paradigm called serverless computing has to offer. These include rapid scalability, no infrastructure management, automatic scaling to zero for idle clients, and a fine-grained pay-per-use billing model. This talk will describe FedLess, an open-source cloud-agnostic framework that enables scalable FL for training arbitrary DNN models using serverless platforms such as AWS Lambda, Google Cloud Functions, and Azure functions. This talk will then discuss the different built-in strategies in FedLess to address common challenges in scalable FL such as stragglers and resource heterogeneity among clients. Finally, a demo for using FedLess to set up a client federation for training will be presented. This talk will be beneficial for those who are interested in FL and want to learn more about serverless computing.

avatar for Mohak Chadha

Mohak Chadha

Research Associate, Technical University of Munich
Mohak Chadha is a final-year Ph.D. student at the Chair of Computer Architecture and Parallel Systems at the Technical University of Munich. He is working in the broad domain of cloud computing, particularly focusing on several challenges in serverless computing such as performance... Read More →

Tuesday September 19, 2023 12:20 - 13:00 CEST
Room 0D-2-0D-3 (Floor 0)
  Open AI & Data Forum