Detection and Adaptation of Staggered Concept Drift in Federated Online Learning - A Dynamic Cluster-Based Framework for Machine Learning with Distributed Heterogenous Data Streams

Abstract

Federated Learning (FL) enables collaborative model training across distributed edge devices without requiring the exchange of raw data. However, most FL approaches assume stationary data distributions, whereas real-world environments often exhibit concept drift, where the relationship between input data and target variables changes over time. In federated settings, such drifts may occur independently across clients, creating staggered concept drift that can lead to model poisoning, degraded predictive performance, and ineffective aggregation. This thesis investigates how staggered concept drift can be automatically detected and adapted to in a federated online learning setting under the computational constraints of edge devices. A drift-aware FL framework is proposed in which clients continuously train local online models, monitor prediction errors using lightweight drift detectors, and perform local adaptation when drift is detected. To prevent aggregation of models representing different concepts, a server-side clustering mechanism dynamically groups clients according to their current concepts and maintains separate cluster models for aggregation. Additionally, the framework is evaluated on the computational cost of the edge-level algorithms, the accuracy of the detectors and performance of the global models.