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Differentially Private Federated Learning for Resource-Constrained Internet of Things
[article]
2020
arXiv
pre-print
With the proliferation of smart devices having built-in sensors, Internet connectivity, and programmable computation capability in the era of Internet of things (IoT), tremendous data is being generated at the network edge. Federated learning is capable of analyzing the large amount of data from a distributed set of smart devices without requiring them to upload their data to a central place. However, the commonly-used federated learning algorithm is based on stochastic gradient descent (SGD)
arXiv:2003.12705v1
fatcat:thczcju5obhrjduow6af4j45qy