A Distributed Cooperation-Competition Learning Algorithm With Neuro-Fuzzy Networks for Latency Communication Networks

As the foundation of next-generation wireless networks, distributed learning (DL) is expected to be integrated into 6 G communication networks, profoundly advancing the transformation of intelligent connectivity. However, network-induced delays critically degrade the performance of DL algorithms in practical deployments. Beyond the communication limitation, many emerging applications involve antagonistic interactions among agents, introducing additional complexity. To overcome these challenges, we propose a decentralized distributed cooperation–competition learning (DCCL) algorithm based on neuro-fuzzy networks, designed for latency communication networks. The algorithm innovatively employs the signed graph to naturally encode the coupling coopetition relationships among agents, and incorporates the delay model into its design. It demonstrates superior adaptability for DL problems with bimodal coalitional adversarial interactions in latency-prone mission-critical services. Moreover, we extend the neuro-fuzzy network into a distributed version, and the resulting distributed neuro-fuzzy model inherently preserves the interpretability characteristic and superior learning capability. Based on structural balance theory and discrete Lyapunov stability theory, we rigorously prove the convergence of the DCCL algorithm and derive an explicit sufficient condition in the form of a maximum allowable latency tolerance. The proposed algorithm benefits privacy protection by transmitting only model parameters. Experiments are conducted to validate the performance of the DCCL algorithm on several datasets for regression and classification. Furthermore, we discuss the limitations of the DCCL algorithm, providing a balanced perspective for future research.