NimbleEdge
🚀 Feature: Supporting multiple federated learning datasets in the library
🔖 Feature description
While at one end we are implementing several state-of-the-art papers in federated learning, we need to support those implementations with readily available datasets. There have been some efforts in building unified access to FL datasets like LEAF, FedML, and others.
One such implementation is found in the recent paper FedScale. It's implementation can be found here.
We need to figure out if we can integrate it seamlessly into our pipeline.
🎤 Pitch
Abstract of the Paper
We present FedScale, a diverse set of challenging and realistic benchmark datasets to facilitate scalable, comprehensive, and reproducible federated learning (FL) research. FedScale datasets are large-scale, encompassing a diverse range of important FL tasks, such as image classification, object detection, word prediction, and speech recognition. For each dataset, we provide a unified evaluation protocol using realistic data splits and evaluation metrics. To meet the pressing need for reproducing realistic FL at scale, we have also built an efficient evaluation platform to simplify and standardize the process of FL experimental setup and model evaluation. Our evaluation platform provides flexible APIs to implement new FL algorithms and includes new execution backends with minimal developer efforts. Finally, we perform in-depth benchmark experiments on these datasets. Our experiments suggest fruitful opportunities in heterogeneity-aware co-optimizations of the system and statistical efficiency under realistic FL characteristics. FedScale is open-source with permissive licenses and actively maintained, and we welcome feedback and contributions from the community
📖 Additional Content
No response
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