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SmartPC: Hierarchical pace control in real-time federated learning system
Li,Li1; Xiong,Haoyi2; Guo,Zhishan3; Wang,Jun4; Xu,Cheng Zhong1,5
Source PublicationProceedings - Real-Time Systems Symposium
AbstractFederated Learning is a technique for learning AI models through the collaboration of a large number of resourceconstrained mobile devices, while preserving data privacy. Instead of aggregating the training data from devices, Federated Learning uses multiple rounds of parameter aggregation to train a model, wherein the participating devices are coordinated to incrementally update a shared model with their own parameters locally learned. To efficiently deploy Federated Learning system over mobile devices, several critical issues including realtimeliness and energy efficiency should be well addressed. This paper proposes SmartPC, a hierarchical online pace control framework for Federated Learning that balances the training time and model accuracy in an energy-efficient manner. SmartPC consists of two layers of pace control: global and local. Prior to every training round, the global controller first oversees the status (e.g., connectivity, availability, and energy/resource remained) of every participating device, then selects qualified devices and assigns them a well-estimated virtual deadline for task completion. Within such virtual deadline, a statistically significant proportion (e.g., 60%) of the devices are expected to complete one round of their local training and model updates, while the overall progress of multi-round training procedure is kept up adaptively. On each device, a local pace controller then dynamically adjusts device settings such as CPU frequency so that the learning task is able to meet the deadline with the least amount of energy consumption. We performed extensive experiments to evaluate SmartPC on both Android smartphones and simulation platforms using well-known datasets. The experiment results show that SmartPC reduces up to 32:8% energy consumption on mobile devices and achieves a speedup of 2.27 in training time without model accuracy degradation.
KeywordEnergy efficiency Federated Learning Real-time scheduling
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Scopus ID2-s2.0-85083189820
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Document TypeConference paper
CollectionFaculty of Science and Technology
Affiliation1.Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen,China
2.Big Data Lab (BDL),Baidu,Inc.,Beijing,China
3.Department of Electrical and Computer Engineering,University of Central Florida,Orlando,United States
4.School of Computer Science,McGill University,Montréal,Canada
5.Department of Computer and Information Science,University of Macau,Macao
Recommended Citation
GB/T 7714
Li,Li,Xiong,Haoyi,Guo,Zhishan,et al. SmartPC: Hierarchical pace control in real-time federated learning system[C],2019:406-418.
APA Li,Li,Xiong,Haoyi,Guo,Zhishan,Wang,Jun,&Xu,Cheng Zhong.(2019).SmartPC: Hierarchical pace control in real-time federated learning system.Proceedings - Real-Time Systems Symposium,2019-December,406-418.
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