Federated Learning and Cooperative Game Theory for Water Resource Management

Abstract

This paper designs a federated learning scheme that combines collaborative game theory to improve the decentralized management of multiple dams. Dams working together can use the FedAS algorithm integrated with cooperative coalitions to solve the issues of water levels while also solving data heterogeneity and resource inconsistencies within the coalition. This helps in resolving issues of predictability and equity of water distribution through optimal resource-sharing goals and adapting to local data patterns. Test results demonstrate better results compared with non-cooperative scenarios, such as a more stable water level and a better balance of water distributed with greater frequency of cooperation which shows great potential for resource management.