Step by Step Guide to Build and Compare FedAvg and FedProx Federated Learning on Non-IID CIFAR-10 with NVIDIA FLARE
Quick Take
This tutorial demonstrates how to build and compare FedAvg and FedProx federated learning models using NVIDIA FLARE on a non-IID CIFAR-10 dataset. By simulating realistic label imbalances through a Dirichlet distribution, the experiment showcases the effectiveness of both methods in handling client data disparities. The NVFlare Job API is utilized for defining and launching federated jobs.
Key Points
- FedAvg and FedProx are compared in a federated learning setup.
- Client data is split using a Dirichlet distribution for realism.
- NVIDIA FLARE is the framework used for the experiment.
- The tutorial provides step-by-step guidance for implementation.
- Focus on non-IID CIFAR-10 dataset to simulate label imbalance.
Article Excerpt
From source RSS / original summaryIn this tutorial, we build an advanced federated learning experiment with NVIDIA FLARE. We compare FedAvg and FedProx on a non-IID CIFAR-10 setup, where client data is split using a Dirichlet distribution to simulate realistic label imbalance across federated sites. We use the NVFlare Job API to define and launch federated jobs, while the Client […] The post Step by Step Guide to Build and Compare FedAvg and FedProx Federated Learning on Non-IID CIFAR-10 with NVIDIA FLARE appeared first on MarkTechPost.
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