TY  - JOUR
T1  - A Non-Disclosure Incentive Mechanism for Federated Learning Via Alternating-offers Bargaining
AU  - Jin, Suyeon 
AU  - Cha, Chaeyeon 
AU  - Park, Hyunggon 
JO  - The Journal of Korean Institute of Communications and Information Sciences
PY  - 2025
DA  - 2025/1/1
DO  - 10.7840/kics.2025.50.9.1343
KW  - Federated learning
KW  - Incentive mechanism
KW  - Alternating-offers bargaining
AB  - In this paper, we propose a non-disclosure incentive mechanism for federated learning via alternating-offers bargaining which does not utilize device information of clients while improving learning performance and approximating an optimal resource allocation. By defining utility functions of clients that reflect their self estimated performances and determining minimum compensation information, clients can be selected for participation without revealing their device information such as loss of their local model or dataset size. To determine the compensation resource allocation, server and clients perform alternating-offers bargaining, which does not require sharing their utility functions. Experiment results show that the proposed incentive mechanism can speed up convergence, improve test accuracy, and induce compensation resource allocation near the Nash bargaining soution while not revealing the device information of clients.