Concept drift detection and adaptation for federated and continual learning

TL;DR

This paper introduces CDA-FedAvg, a novel method for federated learning that detects and adapts to concept drift, improving model performance in non-stationary data environments.

Contribution

It proposes CDA-FedAvg, an extension of FedAvg, specifically designed for continual adaptation to concept drift in federated learning scenarios.

Findings

CDA-FedAvg outperforms standard FedAvg in concept drift scenarios.

Regular FedAvg shows weaknesses under non-stationary data.

Empirical results demonstrate improved adaptation and accuracy.

Abstract

Smart devices, such as smartphones, wearables, robots, and others, can collect vast amounts of data from their environment. This data is suitable for training machine learning models, which can significantly improve their behavior, and therefore, the user experience. Federated learning is a young and popular framework that allows multiple distributed devices to train deep learning models collaboratively while preserving data privacy. Nevertheless, this approach may not be optimal for scenarios where data distribution is non-identical among the participants or changes over time, causing what is known as concept drift. Little research has yet been done in this field, but this kind of situation is quite frequent in real life and poses new challenges to both continual and federated learning. Therefore, in this work, we present a new method, called Concept-Drift-Aware Federated Averaging (CDA-FedAvg). Our proposal is an extension of the most popular federated algorithm, Federated Averaging (FedAvg), enhancing it for continual adaptation under concept drift. We empirically demonstrate the weaknesses of regular FedAvg and prove that CDA-FedAvg outperforms it in this type of scenario.