Adaptive Learning Algorithms for Non-Stationary Environments: Robustness Analysis in Distributed Systems

Abstract

Adaptive learning algorithms are increasingly important as distributed computing infrastructures encounter evolving and non-stationary data streams. Traditional static machine learning models fail to maintain accuracy under drift conditions, prompting demand for adaptive mechanisms capable of adjusting to dynamic environments. This paper examines the robustness of adaptive learning models under multiple forms of concept drift within distributed systems. Sudden, gradual, and recurrent drift types are simulated to evaluate the performance stability of incremental algorithms and ensemble-based models. Drift detection metrics, update frequency, and heterogeneous node behaviors are analyzed to determine how distributed learning frameworks behave under constrained computing resources. Results demonstrate that combining lightweight drift detection with incremental updating yields improved resilience in non stationary conditions. The findings provide insights applicable to teleoperations, remote analytics, and distributed decision systems.