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Federated Learning (FL) is a promising decentralized machine learning technique, which can be efficiently used to reduce the latency and deal with the data privacy in the next 6th generation (6G) of wireless networks. However, the finite computation and communication resources of the wireless devices, is a limiting factor for their very low latency requirements, while users need incentives for spending their constrained resources. In this direction, we propose an incentive mechanism for Wireless FL (WFL), which motivates users to utilize their available radio and computation resources, in order to achieve a fast global convergence of the WFL process. More specifically, we model the interaction among users and the server as a Stackelberg game, where users (followers) aim to maximize their utility/pay-off, while the server (leader) focuses on minimizing the global convergence time of the FL task. We analytically solve the Stackelberg game and derive the optimal strategies for both the server and the user set, corresponding to the Stackelberg equilibrium. Following that, we consider the presence of malicious users, who may attempt to mislead the server with false information throughout the game, aiming to further increase their utility. To alleviate this burden, we propose a deep learning-aided secure mechanism at the servers’ side, which detects malicious users and prevents them from participating into the WFL process. Simulations verify the effectiveness of the proposed method, which result in increased users’ utility and reduced global convergence time, compared with various baseline schemes. Finally, the proposed mechanism for detecting the users’ behavior seems to be very promising in increasing the security of WFL-based networks.

Incentive-Based Delay Minimization for 6G-Enabled Wireless Federated Learning