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De-centralized Learning for Radio Network Key Performance Indicator Prediction

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Title: De-centralized Learning for Radio Network Key Performance Indicator Prediction
Author(s): Alcantara, Jose Carlos
Contributor: University of Helsinki, Faculty of Science, Tietojenkäsittelytieteen osasto
Discipline: Algorithms and Machine Learning
Language: English
Acceptance year: 2020
A recent machine learning technique called federated learning (Konecny, McMahan, et. al., 2016) offers a new paradigm for distributed learning. It consists of performing machine learning on multiple edge devices and simultaneously optimizing a global model for all of them, without transmitting user data. The goal for this thesis was to prove the benefits of applying federated learning to forecasting telecom key performance indicator (KPI) values from radio network cells. After performing experiments with different data sources' aggregations and comparing against a centralized learning model, the results revealed that a federated model can shorten the training time for modelling new radio cells. Moreover, the amount of transferred data to a central server is minimized drastically while keeping equivalent performance to a traditional centralized model. These experiments were performed with multi-layer perceptron as model architecture after comparing its performance against LSTM. Both, input and output data were sequences of KPI values.
Keyword(s): machine learning artificial neural networks regression time series forecasting deep learning

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