In a lattice-based simulation of decentralized federated learning, a reputation mechanism that rewards cooperators and penalizes defectors raises average accuracy from 70% to 82% and drives cooperation to near 100%.
IEEE Security & Privacy17, 49–58 (2018)
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Reputation-driven Cooperation in Lattice-based Decentralized Federated Learning through Evolutionary Game Theory
In a lattice-based simulation of decentralized federated learning, a reputation mechanism that rewards cooperators and penalizes defectors raises average accuracy from 70% to 82% and drives cooperation to near 100%.