Adaptive Federated Learning in Presence of Concept Drift

Federated Learning (FL) is a promising research area in the machine learning field. Techniques and solutions belonging to this area operate in distributed scenarios, comprising a server and pervasively distributed clients, aiming at learning a single central model without sending (possibly sensitive) data from the clients to the server. Such an approach allows mitigating the privacy concerns that are nowadays perceived as relevant in distributed machine learning solutions leveraging data belonging to different users or companies. The literature in the field of FL is wide and many state-of-the-art solutions are available. Unfortunately, all these solutions assume (implicitly or explicitly) that the process generating the data is stationary (hence not changing its statistical behavior over time); an assumption that rarely holds in real-world conditions where concept drift occurs due to, e.g., seasonality or periodicity effects, faults in sensors or actuators or changes in the users' behaviour. In this paper, we introduce, for the first time in the literature, a novel FL algorithm called Adaptive-FedAVG, able to operate with nonstationary data generating processes affected by concept drifts. Following a passive approach, Adaptive-FedAVG is able to increase the accuracy in stationary conditions and promptly react to concept drift by adapting the learning rate to increase the plasticity of the learning phase. A wide experimental campaign shows the effectiveness of the proposed Adaptive-FedAVG algorithm by comparing it with a state-of-the-art FL algorithm present in the literature both in stationary and non-stationary conditions.