  GTFLAT, as a game theory-based add-on, addresses an important research
question: How can a federated learning algorithm achieve better performance and
training efficiency by setting more effective adaptive weights for averaging in
the model aggregation phase? The main objectives for the ideal method of
answering the question are: (1) empowering federated learning algorithms to
reach better performance in fewer communication rounds, notably in the face of
heterogeneous scenarios, and last but not least, (2) being easy to use
alongside the state-of-the-art federated learning algorithms as a new module.
To this end, GTFLAT models the averaging task as a strategic game among active
users. Then it proposes a systematic solution based on the population game and
evolutionary dynamics to find the equilibrium. In contrast with existing
approaches that impose the weights on the participants, GTFLAT concludes a
self-enforcement agreement among clients in a way that none of them is
motivated to deviate from it individually. The results reveal that, on average,
using GTFLAT increases the top-1 test accuracy by 1.38%, while it needs 21.06%
fewer communication rounds to reach the accuracy.

Machine Learning;Artificial Intelligence;Distributed, Parallel, and Cluster Computing;Computer Science and Game Theory