Driving Cooperation in Federated Learning via Evolutionary Game Theory

Manuel Röder, Fabian Geiger, Frank-Michael Schleif

Abstract: We introduce an enhanced formulation of FedT4T-Pro, a Federated Learning framework designed to systematically assess utility-driven client strategies within resource-constrained environments. To address key challenges in practical distributed learning systems, such as resource limitations and non-cooperative behaviors, we model client interactions using the Iterated Prisoner’s Dilemma. Our framework empowers clients to refine their decision rules based on past interactions and available resources, optimizing both individual utility and overall contributions to a global learning objective. In addition, a novel sampling algorithm for client selection, drawing inspiration from evolutionary biology, is proposed as a natural incentive mechanism to encourage consistent cooperation and resource contributions. We apply FedT4T-Pro to a Federated Learning benchmark classification task and explore the dynamics of cooperation between clients driven by common strategies from Cooperation theory under the impact of varying resource availability. Furthermore, we experimentally show that the proposed sampling algorithm fosters collaborative behavior in FL training.

External IDs:dblp:conf/ijcnn/RoderGS25