  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.
