Energy-Efficient and Learning-Aware Edge Intelligence With Probabilistic Scheduling

In edge intelligence networks, various applications exist based on different users and datasets. To realize intelligence, many works design various edge learning models to process these data. However, different types of users and models have negative influence on the learning performance, even degrading the learning gain. To address these issues, we propose a learning-aware optimization model with probabilistic scheduling. First, we design users scheduling to reveal the relationships between resource allocation and learning performance. Then, we also develop a fast proximal linearized algorithm in the large-scale communication networks. Next, we derive tasks scheduling in asymptotic cases to improve resource utilization efficiency. At last, extensive experimental results corroborate that probabilistic scheduling and multi-objective learning models improve the distinct edge learning gain efficiently compared with the state-of-the-art benchmark algorithms.