Detect, adapt, overcome: mitigating concept drift in federated learning

Federated Learning (FL) is a distributed machine learning paradigm where a central server coordinates the training of a global model across multiple decentralized clients. Most FL algorithms assume stationary data-generating processes and neglect concept drift, i.e., the change of data distributions over time. This results in a gradual decline of the model's performance and eventually renders the model obsolete. We investigate the impact of concept drift in FL and propose an active approach to detect and adapt to drift. Our detection method is based on local model loss which allows us to categorize clients based on the type of drift they experience and apply appropriate adaptive measures. Through extensive experiments, we demonstrate the efficiency, accuracy, and precision of our approach: the proposed adaptation approach successfully accelerates convergence for drifted clients, outperforming the baseline algorithm. Even under extreme drift conditions, our global model remains stable. This demonstrates the superior robustness of our approach compared to conventional methods.

Subjects

Machine Learning

Federated Learning

Concept Drift

Model Adaptation

Drift Detection

Non-IID Data

DDC Class

600: Technology

006.31: Machine Learning

Funding(s)

Christian Doppler Labor für Blockchaintechnologien für das Internet der Dinge

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Detect, adapt, overcome: mitigating concept drift in federated learning