C. -F. Liu and M. Bennis, "Federated Learning with Correlated Data: Taming the Tail for Age-Optimal Industrial IoT," 2021 19th International Symposium on Modeling and Optimization in Mobile, Ad hoc, and Wireless Networks (WiOpt), 2021, pp. 1-6, doi: 10.23919/WiOpt52861.2021.9589092
Federated learning with correlated data : taming the tail for age-optimal industrial IoT
|Author:||Liu, Chen-Feng1; Bennis, Mehdi1|
1Centre for Wireless Communications, University of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.5 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022032124224
|Publish Date:|| 2022-03-21
While information delivery in industrial Internet of things demands reliability and latency guarantees, the freshness of the controller’s available information, measured by the age of information (AoI), is paramount for high-performing industrial automation. The problem in this work is cast as a sensor’s transmit power minimization subject to the peak-Aol requirement and a probabilistic constraint on queuing latency. We further characterize the tail behavior of the latency by a generalized Pareto distribution (GPD) for solving the power allocation problem through Lyapunov optimization. As each sensor utilizes its own data to locally train the GPD model, we incorporate federated learning and propose a local-model selection approach which accounts for correlation among the sensor’s training data. Numerical results show the tradeoff between the transmit power, peak AoI, and delay’s tail distribution. Furthermore, we verify the superiority of the proposed correlation-aware approach for selecting the local models in federated learning over an existing baseline.
|Pages:||1 - 6|
19th International Symposium on Modeling and Optimization in Mobile, Ad hoc, and Wireless Networks, WiOpt 2021
International Symposium on Modeling and Optimization in Mobile, Ad hoc, and Wireless Networks
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This research was supported by the Academy of Finland project MISSION, the Academy of Finland project
SMARTER, the CHIST-ERA project LeadingEdge under Grant CHIST-ERA-18-SDCDN-004, the CHIST-ERA project CONNECT, the INFOTECH project NOOR, and the Nokia Bell Labs project NEGEIN.
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