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Machine learning (ML) is expected to play a major role in 5G edge computing.Various studies have demonstrated that ML is highly suitable for optimizingedge computing systems as rapid mobility and application-induced changes occurat the edge. For ML to provide the best solutions, it is important tocontinually train the ML models to include the changing scenarios. The suddenchanges in data distributions caused by changing scenarios (e.g., 5G basestation failures) is referred to as concept drift and is a major challenge tocontinual learning. The ML models can present high error rates while the driftstake place and the errors decrease only after the model learns thedistributions. This problem is more pronounced in a distributed setting wheremultiple ML models are being used for different heterogeneous datasets and thefinal model needs to capture all concept drifts. In this paper, we show thatusing Attention in Federated Learning (FL) is an efficient way of handlingconcept drifts. We use a 5G network traffic dataset to simulate concept driftand test various scenarios. The results indicate that Attention cansignificantly improve the concept drift handling capability of FL.

Attentive Federated Learning for Concept Drift in Distributed 5G Edge Networks