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Item type:Publication, Access status: without files , Adaption via selection: on client selection to counter concept drift in federated learningFederated Learning is a Machine Learning paradigm in which multiple devices jointly train a shared model without the need to share their local data. Most Federated Learning approaches assume that the data remains static. However, this assumption is unrealistic in real-world scenarios where data may change over time. In this paper we study the impact of concept drift on Federated Learning models with heterogeneous data distribution. Client selection directly impacts the model’s performance, and we observe that the local loss of clients under concept drift deviates. Nevertheless, most applications select clients randomly for each training round. Our main contribution therefore is a probabilistic client selection algorithm that introduces a bias towards clients with higher local loss in order to counter concept drift. Extensive evaluations show that our algorithm recovers a drop in accuracy incurred by concept drift up to five times faster than random sampling while retaining competitive performance.Publicationtype: Conference Paper72 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Client-side adaptation to concept drift in federated learningFederated Learning is a paradigm at the intersection of Machine Learning and Distributed Computing. The fundamental idea is that multiple agents collaborate to train a common model without sharing their local data. In this work, we propose an algorithm that passively adapts to concept drift, the evolution of data over time. Our algorithm is deployed on the client side. The main idea is to use a dynamic learning rate at each client which adapts automatically in presence of concept drift. To calculate the learning rate we utilizes the individual training loss. We present an evaluation based on multiple datasets and two types of concept drift, sudden drift and incremental drift. Our empirical analysis shows that the algorithm mitigates the impact of concept drift on the model accuracy and reduces the recovery time required to regain the original level of accuracy.Publicationtype: Conference Paper92