Communication-Efficient Federated Learning in Channel Constrained Internet of Things
Hu, T., Zhang, X., Chang, Z., Hu, F., & Hämäläinen, T. (2022). Communication-Efficient Federated Learning in Channel Constrained Internet of Things. In GLOBECOM 2022 IEEE Global Communications Conference (pp. 275-280). IEEE. https://doi.org/10.1109/globecom48099.2022.10000898
Hu, Tao |
DisciplineTekniikkaTietotekniikkaSecure Communications Engineering and Signal ProcessingEngineeringMathematical Information TechnologySecure Communications Engineering and Signal Processing
© 2022, IEEE
Federated learning (FL) is able to utilize the computing capability and maintain the privacy of the end devices by collecting and aggregating the locally trained learning model parameters while keeping the local personal data. As the most widely-used FL framework,Jederated averaging (FedAvg) suffers an expensive communication cost especially when there are large amounts of devices involving the FL process. Moreover, when considering asynchronous FL, the slowest device becomes the bottleneck for the cask effect and determines the overall latency. In this work, we propose a communication-efficient federated learning framework with partial model aggregation (CE-FedPA) algorithm to utilize compression strategy and weighted device selection, which can significantly reduce the size of uploaded data and decrease the communication time. We perform a series of experiments on the MNIST/CIFAR-10 datasets, in both lID and non-lID data settings. We compare the communication time of different aggregation schemes, in terms of iteration rounds and target accuracy. Simulation results demonstrate that the uploading time of the proposed scheme is up to 4.3 times shorter than other existing ones. Experiments on an end - to-end FL framework also verify the communication efficiency of CE-FedPA in a real-world setting. ...
Parent publication ISBN978-1-6654-3541-3
ConferenceIEEE Global Communications Conference
Is part of publicationGLOBECOM 2022 IEEE Global Communications Conference
Publication in research information system
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