Alternating Offer-Based Payment Allocation for Privacy Non-Disclosure in Federated Learning

In federated learning (FL), it is essential to implement a payment allocation mechanism that compensates clients for the costs incurred from participating in FL tasks. In this letter, we formulate the payment allocation as a bargaining game between a global server and clients and adopt the Nash bargaining solution (NBS) to achieve optimal and fair payment assignments among clients. Unlike existing payment allocation mechanisms that require the disclosure of private information from the clients, the proposed approach ensures privacy non-disclosure for bargaining. The key idea is to decompose the one-to-many bargaining game into independent one-to-one bargaining games and use alternating-offers, which do not require the disclosure of private information from clients. We design an alternating-offers strategy and acceptance criteria to ensure fair agreements without the private information of clients. Simulation results show that the proposed payment allocation strategy can fairly allocate payments to clients while maintaining the accuracy of the global server in FL tasks.