The evolution of resource-aware cooperation in federated learning

We present an extended formulation of our base framework FedT4T-Evo ; a Federated Learning approach that systematically evaluates utility-driven client strategies under resource limitations. To address challenges in distributed learning systems, including resource constraints and non- cooperative behaviors, we model client interactions through the Iterated Prisoner’s Dilemma. Our framework enables clients to adapt decision rules based on prior interactions and resource availability, optimizing both individual utility and contributions to the global optimization target. To further extend the natural perspective, we present a novel evolutionary selection algorithm that simulates ecological dynamics over populations of client strategies, providing a instinctive mechanism for the emergence and persistence of cooperation. Applied to benchmark tasks, our experimental results showed that the framework offers an effective approach for gaining insights into Federated Learning systems through the lens of cooperation theory. • A cooperation-theoretic FL framework is proposed to natively study and foster collaborative client behaviors. • Resource-aware mechanisms are introduced to adapt to heterogeneity and constraints in real-world FL environments. • Evolutionary dynamics are integrated to simulate ecological patterns of cooperation across FL clients.