FL has emerged as a promising technique for edge devices to collaboratively learn a shared prediction model while keeping their training data on the device, thereby decoupling machine learning from the need to store the data in the cloud. However, FL is difficult to realistically implement due to scale and system heterogeneity. Although there are several research frameworks for simulating FL algorithms, none of them support the study of scalable FL workloads on heterogeneous edge devices.
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FL has emerged as a promising technique for edge devices to collaboratively learn a shared prediction model while keeping their training data on the device, thereby decoupling machine learning from the need to store the data in the cloud. However, FL is difficult to realistically implement due to scale and system heterogeneity. Although there are several research frameworks for simulating FL algorithms, none of them support the study of scalable FL workloads on heterogeneous edge devices.