NVIDIA FLARE tutorial compares FedAvg and FedProx on non-IID data

This tutorial demonstrates how to implement and compare the FedAvg and FedProx federated learning algorithms using NVIDIA FLARE. The experiment utilizes a non-IID CIFAR-10 dataset, simulated by partitioning data with a Dirichlet distribution to mimic realistic label imbalance across clients. The guide details setting up the NVFlare environment, defining client-side scripts for local training and model exchange, and visualizing the global model's accuracy progression over training rounds. AI

影响 Provides a practical guide for researchers and developers to implement and compare federated learning algorithms, highlighting differences in performance on imbalanced data.

排序理由 The article is a technical tutorial demonstrating a specific implementation of federated learning algorithms on a benchmark dataset. [lever_c_demoted from research: ic=1 ai=1.0]

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• CIFAR-10
• FedAvg
• FedProx

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