[WEBINAR] Optimising Federated Learning Workloads: A Practical Evaluation
Intel® Software is hosting an exciting session with an in-depth evaluation of Intel CPU and GPU performance in federated learning (FL). FL is a machine learning approach that trains models on decentralised edge devices without sharing raw data, preserving data privacy while allowing collaborative model development.
Challenges Addressed: The session focuses on optimising FL workload performance on CPUs and GPUs, using a recent ASUS FL solution for the healthcare industry. This solution enhances efficiency and performance with Intel® Xeon® processors and Intel® oneAPI AI tools.
Key learnings:
- Overview of the ASUS solution, including approach, methodologies, and results.
- Using Intel AI software stacks to improve CNN model performance in medical scenarios.
- Addressing AI framework performance issues and hardware compatibility.
- Training a CNN model collaboratively without sharing private data, using the open-source FL framework Flower.
- Skill Level: All
Featured software in the webinar:
- Intel® oneAPI Deep Neural Network Library - standalone or as part of the Intel® oneAPI Base Toolkit
- Intel® VTune™ Profiler - standalone or as part of the Intel® oneAPI Base Toolkit
- Intel® Distribution for Python
- Intel® Extension for PyTorch
Sign up for the Federated Learning webinar
This webinar takes place on Wednesday 7 August 2024 at 5 pm BST.
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Leanne Bevan
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