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Federated learning for performance prediction in multi-operator environments
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Authors: Xiaoyu Lan, Jalil Taghia, Farnaz Moradi, Mohammad Ali Khoshkholghi, Edvin Listo Zec, Olof Mogren, Toktam Mahmoodi, Andreas Johnsson
Status: Final
Date of publication: 10 March 2023
Published in: ITU Journal on Future and Evolving Technologies, Volume 4 (2023), Issue 1, Pages 166-177
Article DOI : https://doi.org/10.52953/PFYZ9165
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Abstract:
Telecom vendors and operators deliver services with strict requirements on performance, over complex and sometimes partly shared network infrastructures. A key enabler for network and service management in such environments is knowledge sharing, and the use of data-driven models for performance prediction, forecasting, and troubleshooting. In this paper, we outline a multi-operator service metrics prediction framework using federated learning that allows privacy-preserved knowledge-sharing across operators for improved model performance, and also reduced requirements on data transfer within an operator network. Federated learning is compared against local and central learning strategies for multi-operator performance prediction, and it is shown to balance the requirements on data privacy, model performance, and the network overhead. Further, the paper provides insights on how data heterogeneity affects model performance, where the conclusion is that standard federated learning has certain robustness to data heterogeneity. Finally, we discuss the challenges related to training a federated learning model with a limited budget on the communication rounds. The evaluation is performed using a set of realistic publicly available data traces, that are adapted specifically for the purpose of studying multi-operator service performance prediction. |
Keywords: Federated learning, machine learning, network automation and management, performance modeling
Rights: © International Telecommunication Union, available under the CC BY-NC-ND 3.0 IGO license.
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