[Coming Aug 7] Optimizing Federated Learning Workloads

 

[Time zone converter] [Available On-Demand on Thursday, August 8] Federated learning (FL) is an ML approach for training models on decentralized edge devices without sharing private data. Benefits include allowing organizations to collaborate on model development, expanding the data set on which models are trained, and preserving data privacy. It’s both cutting edge and challenging. This session addresses one of the challenges: How to optimize FL workload performance on CPUs and GPUs. It illustrates by evaluating the implementation of an ASUS FL solution for healthcare. Key learnings: An overview of the ASUS solution—the approach, methodologies, and results. Using Intel AI software stacks and tools to analyze and enhance the performance of CNN models, e.g., VGG19 and EfficientNet, in real medical scenarios. Reviewing performance issues of AI framework and verifying the compatibility of the desired hardware. Collaboratively training a CNN model without sharing private data and images, using the open source FL framework Flower Skill level: All Register now Featured software 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 More resources ASUS – Unleashing the Power of Intel CPUs in Federated Learning: A Comprehensive Evaluation [PDF]

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