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Conventional machine learning (ML) models provide many benefits to mobile network operators, particularly in terms of ensuring consistent QoE. However, on top of creating a substantial network footprint, the large data transfer required by conventional ML models can be problematic from a data privacy perspective. Federated learning (FL) makes it possible to overcome these challenges by bringing “the computation to the data” instead of transferring “the data to the computation.”
The latest Ericsson Technology Review article demonstrates that it is possible to migrate from a conventional ML model to an FL model and significantly reduce the amount of information that is exchanged between different parts of the network, enhancing privacy without negatively impacting accuracy.