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Cross-silo federated learning (FL) is a typical FL that enablesorganizations(e.g., financial or medical entities) to train global models onisolated data. Reasonable incentive is key to encouraging organizations tocontribute data. However, existing works on incentivizing cross-silo FL lackconsideration of the environmental dynamics (e.g., precision of the trainedglobal model and data owned by uncertain clients during the trainingprocesses). Moreover, most of them assume that organizations share privateinformation, which is unrealistic. To overcome these limitations, we propose anovel adaptive mechanism for cross-silo FL, towards incentivizing organizationsto contribute data to maximize their long-term payoffs in a real dynamictraining environment. The mechanism is based on multi-agent reinforcementlearning, which learns near-optimal data contribution strategy from the historyof potential games without organizations' private information. Experimentsdemonstrate that our mechanism achieves adaptive incentive and effectivelyimproves the long-term payoffs for organizations.

Adaptive incentive for cross-silo federated learning: A multi-agent reinforcement learning approach