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ML Systems for Large Models and Federated Learning

Qirong Ho · Samuel Horvath · Hongyi Wang

East 5


This tutorial will teach attendees how to overcome performance, cost, privacy and robustness challenges when using distributed and federated software systems for learning and deploying Computer Vision and ML applications across various hardware settings (networked machines, GPUs, embedded, mobile systems). The audience will learn about theory, implementation and practice of these topics: state-of-the-art approaches and system architectures, forms of distributed parallelism, pitfalls in the measurement of parallel application performance, parallel ML compilers, computation-communication-memory efficiency in federated learning (FL), trustworthy FL, tackling device heterogeneity in FL, and on-device FL systems.

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