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Federated Learning (FL) has emerged as a promising technique for edge devicesto collaboratively learn a shared prediction model, while keeping theirtraining data on the device, thereby decoupling the ability to do machinelearning from the need to store the data in the cloud. However, FL is difficultto implement realistically, both in terms of scale and systems heterogeneity.Although there are a number of research frameworks available to simulate FLalgorithms, they do not support the study of scalable FL workloads onheterogeneous edge devices.  In this paper, we present Flower -- a comprehensive FL framework thatdistinguishes itself from existing platforms by offering new facilities toexecute large-scale FL experiments and consider richly heterogeneous FL devicescenarios. Our experiments show Flower can perform FL experiments up to 15M inclient size using only a pair of high-end GPUs. Researchers can then seamlesslymigrate experiments to real devices to examine other parts of the design space.We believe Flower provides the community with a critical new tool for FL studyand development.

Flower: A Friendly Federated Learning Research Framework