In vanilla federated learning (FL) such as FedAvg, the parameter server (PS) and multiple distributed clients can form a typical buyer's market, where the number of PS/buyers of FL services is far less than the number of clients/sellers. In order to improve the performance of FL and reduce the cost of motivating clients to participate in FL, this paper proposes to differentiate the pricing for services provided by different clients rather than simply providing the same service pricing for different clients. The price is differentiated based on the performance improvements brought to FL and their heterogeneity in computing and communication capabilities. To this end, a price-discrimination game (PDG) is formulated to comprehensively address the distributed resource management problems in FL, including multi-objective trade-off, client selection, and incentive mechanism. As the PDG is a mixed-integer nonlinear programming (MINLP) problem, a distributed semi-heuristic algorithm with low computational complexity and low communication overhead is designed to solve it. The simulation result verifies the effectiveness of the proposed approach.

翻译：在诸如FedAvg等经典联邦学习中，参数服务器与多个分布式客户端可形成典型的买方市场，即提供联邦学习服务的参数服务器（买方）数量远少于承担模型训练的客户端（卖方）数量。为提升联邦学习性能并降低激励客户端参与联邦学习的成本，本文提出对不同客户端提供的服务实施差异化定价，而非对所有客户端提供统一服务定价。具体定价依据各客户端对联邦学习性能提升的贡献及其计算与通信能力的异质性进行区分。为此，构建了价格歧视博弈模型，系统性地解决联邦学习中的分布式资源管理问题，包括多目标权衡、客户端选择及激励机制。由于该博弈属于混合整数非线性规划问题，本文设计了一种具有低计算复杂度和低通信开销的分布式半启发式算法进行求解。仿真结果验证了所提方法的有效性。