FedScale: Benchmarking Model and System Performance of Federated Learning at Scale
This addresses the need for standardized and scalable benchmarking in federated learning research, though it is incremental as it builds on existing FL frameworks.
FedScale introduces a federated learning benchmarking suite with realistic datasets and a scalable runtime to enable reproducible research across tasks like image classification and language modeling, providing high-level APIs for easy deployment and evaluation.
We present FedScale, a federated learning (FL) benchmarking suite with realistic datasets and a scalable runtime to enable reproducible FL research. FedScale datasets encompass a wide range of critical FL tasks, ranging from image classification and object detection to language modeling and speech recognition. Each dataset comes with a unified evaluation protocol using real-world data splits and evaluation metrics. To reproduce realistic FL behavior, FedScale contains a scalable and extensible runtime. It provides high-level APIs to implement FL algorithms, deploy them at scale across diverse hardware and software backends, and evaluate them at scale, all with minimal developer efforts. We combine the two to perform systematic benchmarking experiments and highlight potential opportunities for heterogeneity-aware co-optimizations in FL. FedScale is open-source and actively maintained by contributors from different institutions at http://fedscale.ai. We welcome feedback and contributions from the community.