qub-blesson/FedAdapt
Adaptive Offloading of Federated Learning on IoT Devices
This framework helps data scientists and machine learning engineers accelerate federated learning model training on networks of diverse Internet of Things (IoT) devices. It takes your federated learning setup, including a deep neural network model and IoT devices with varying computational power and network conditions, and outputs an optimized training process. The result is faster model convergence and reduced training time for distributed machine learning applications.
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Use this if you are deploying federated learning models across many IoT devices and struggle with slow training due to device performance differences or inconsistent network connectivity.
Not ideal if your federated learning setup does not involve resource-constrained IoT devices or highly variable network environments.
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Language
Python
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Last pushed
Nov 18, 2022
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