[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.

Sign up today!

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