Sixth-generation(6G)communication system promises unprecedented data density and transformative applications over different industries.However,managing heterogeneous data with different distributions in 6G-enabled mul...Sixth-generation(6G)communication system promises unprecedented data density and transformative applications over different industries.However,managing heterogeneous data with different distributions in 6G-enabled multi-access edge cloud networks presents challenges for efficient Machine Learning(ML)training and aggregation,often leading to increased energy consumption and reduced model generalization.To solve this problem,this research proposes a Weighted Proximal Policy-based Federated Learning approach integrated with Res Net50 and Scaled Exponential Linear Unit activation function(WPPFL-RS).The proposed method optimizes resource allocation such as CPU and memory,through enhancing the Cyber-twin technology to estimate the computing capacities of edge clouds.The proposed WPPFL-RS approach significantly minimizes the latency and energy consumption,solving complex challenges in 6G-enabled edge computing.This makes sure that efficient resource utilization and enhanced performance in heterogeneous edge networks.The proposed WPPFL-RS achieves a minimum latency of 8.20 s on 100 tasks,a significant improvement over the baseline Deep Reinforcement Learning(DRL),which recorded 11.39 s.This approach highlights its potential to enhance resource utilization and performance in 6G edge networks.展开更多
文摘Sixth-generation(6G)communication system promises unprecedented data density and transformative applications over different industries.However,managing heterogeneous data with different distributions in 6G-enabled multi-access edge cloud networks presents challenges for efficient Machine Learning(ML)training and aggregation,often leading to increased energy consumption and reduced model generalization.To solve this problem,this research proposes a Weighted Proximal Policy-based Federated Learning approach integrated with Res Net50 and Scaled Exponential Linear Unit activation function(WPPFL-RS).The proposed method optimizes resource allocation such as CPU and memory,through enhancing the Cyber-twin technology to estimate the computing capacities of edge clouds.The proposed WPPFL-RS approach significantly minimizes the latency and energy consumption,solving complex challenges in 6G-enabled edge computing.This makes sure that efficient resource utilization and enhanced performance in heterogeneous edge networks.The proposed WPPFL-RS achieves a minimum latency of 8.20 s on 100 tasks,a significant improvement over the baseline Deep Reinforcement Learning(DRL),which recorded 11.39 s.This approach highlights its potential to enhance resource utilization and performance in 6G edge networks.
