Shypd.aiThe largest open index of AI toolsNIID-Bench is an Open Source & Models tool that provides a benchmark for federated learning algorithms. It enables experimental studies on non-IID data silos, implementing various federated learning algorithms and non-IID settings.
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NIID-Bench is an open-source benchmark designed for experimental studies in federated learning, specifically focusing on scenarios with non-IID (non-independent and identically distributed) data silos. The tool implements four popular federated learning algorithms: FedAvg, FedProx, SCAFFOLD, and FedNova. It supports three types of non-IID settings, including label distribution skew, feature distribution skew, and quantity skew, across nine diverse datasets such as MNIST, Cifar-10, and FEMNIST. Researchers can use NIID-Bench to evaluate and compare the performance of different federated learning algorithms under various challenging data distribution conditions, contributing to advancements in the field. The project also includes follow-up works like FedOV and FedConcat, and hosts a challenge for researchers to test their algorithms.
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