Resource-Aware Cooperation in Federated Learning

We present a novel Federated Learning framework, FedT4T, that systematically evaluates utility-driven client strategies under resource constraints.Recognizing the significant challenges in practical distributed learning environments, such as limited resources and non-cooperative behaviors, we model client interactions using the Iterated Prisoner's Dilemma.Our framework enables clients to adapt their decision rules based on prior interactions and available resources, optimizing both individual utility and collective contribution to solve a global learning task.We apply FedT4T 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.