论文标题
通过货币预算有限的无线网络，基于学习的客户选择，用于联合学习服务
Learning-Based Client Selection for Federated Learning Services Over Wireless Networks with Constrained Monetary Budgets
论文作者
论文摘要
我们研究了无线网络中多个联合学习（FL）服务的数据质量感知动态客户选择问题，每个客户提供了动态数据集，用于同时培训多个FL服务，而每种FL服务Demander必须在货币预算受限制的情况下为客户付费。在训练回合中，这个问题被正式化为不合作的马尔可夫游戏。提出了一种基于多代理的混合增强算法，以优化共同的客户选择和付款操作，同时避免采取行动冲突。仿真结果表明，我们提出的算法可以显着提高训练性能。
We investigate a data quality-aware dynamic client selection problem for multiple federated learning (FL) services in a wireless network, where each client offers dynamic datasets for the simultaneous training of multiple FL services, and each FL service demander has to pay for the clients under constrained monetary budgets. The problem is formalized as a non-cooperative Markov game over the training rounds. A multi-agent hybrid deep reinforcement learning-based algorithm is proposed to optimize the joint client selection and payment actions, while avoiding action conflicts. Simulation results indicate that our proposed algorithm can significantly improve training performance.
