<ul><li>Researchers propose DualGFL, a novel Federated Learning framework with a Dual-level Game in cooperative-competitive environments.</li><li>DualGFL includes a lower-level hedonic game where clients form coalitions and an upper-level multi-attribute auction game where coalitions bid for training participation.</li><li>At the lower-level, DualGFL introduces a new auction-aware utility function and a Pareto-optimal partitioning algorithm to find a Pareto-optimal partition based on clients' preference profiles.</li><li>At the upper-level, DualGFL formulates a multi-attribute auction game with resource constraints and derives equilibrium bids to maximize coalitions' winning probabilities and profits.</li></ul>

DualGFL: Federated Learning with a Dual-Level Coalition-Auction Game

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