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Item type:Publication, Access status: without files , FLUID: federated learning with unlearning and instant drift-recoveryFederated Learning (FL) enables collaborative model training without sharing raw data, but struggles with concept drift, where client data distributions evolve over time. Existing approaches either retain outdated knowledge or suffer from high computational costs due to frequent retraining. This work introduces Federated Learning with Unlearning and Instant Driftrecovery (FLUID), a novel framework that treats concept drift as a selective forgetting problem. By leveraging techniques from Federated Unlearning, FLUID removes obsolete knowledge while adapting to new data patterns. It combines the lightweight unlearning capability of Federated Auxiliary Unlearning (FedAU) with the rapid recovery capability of Rapid Retraining (RRT), without requiring full retraining. Experimental results show that FLUID achieves balanced, all-around performance matching RRT in recovery speed and better convergence, and FedAU in lower accuracy dips during drift, and it outperforms natural recovery capabilities of Federated Averaging (FedAvg), and retraining from scratch.Publicationtype: Conference Paper81 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Detect, adapt, overcome: mitigating concept drift in federated learningFederated 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.Publicationtype: Conference Paper61