Step by Step Guide to Build and Compare FedAvg and FedProx Federated Learning on Non-IID CIFAR-10 with NVIDIA FLARE
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
IMPACT Provides a practical guide for researchers and developers to implement and compare federated learning algorithms, highlighting differences in performance on imbalanced data.
