Privacy-aware decentralized multi-slice traffic forecasting

Privacy-aware decentralized multi-slice traffic forecasting

Hnin Pann Phyu, Diala Naboulsi, and Razvan Stanica. 2022. Privacy-aware decentralized multi-slice traffic forecasting. In Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services (MobiSys ’22). Association for Computing Machinery, New York, NY, USA, 537–538. https://doi.org/10.1145/3498361.3538772

Abstract or Summary

In this work, taking the perspective of Mobile Virtual Network Operators (MVNOs), we tackle the multi-slice traffic forecasting problem, while respecting the data privacy of users. To this end, we propose the Federated Proximal Long Short-Term Memory (FPLSTM) framework, which allows MVNOs to train at each base station their local models with their private datasets, without compromising data privacy. Prediction results obtained by evaluating the models on a real-world dataset indicate that the forecast of FPLSTM is as accurate as state-of-the-art solutions while ensuring data privacy as well as computation and communication costs efficiency.

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