Federated traffic prediction for 5G and beyond
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Traffic prediction is one of the key ingredients to fulfill some of the goals of next-generation communications systems and to realize self-adaptability in mobile networks. The vast population of devices connected to base stations, while providing rich data to train Machine Learning (ML) models, can also compromise the efficiency of such models (e.g., temporal responsiveness) due to potential communication bottlenecks. In this regard, Federated Learning (FL) arises as a compelling solution to provide robust ML optimization while maintaining the communication overhead low. In FL, devices exchange model parameters, rather than raw training data, thus also enhancing privacy.
This talk will describe a problem statement that proposes the usage of FL tools to predict the traffic in cellular networks collaboratively. Furthermore, we will describe the dataset provided that contains data from unknown LTE users of commercial operators at three different specific locations.
This live event includes a 30-minute networking event hosted on the AI for Good Neural Network. This is your opportunity to ask questions, interact with the panelists and participants and build connections with the AI for Good community.