Deep Learning for Distributed Optimization: Applications to Wireless Resource Management

By Hoon Lee, Sang Hyun Lee, Tony Q. S. Quek. 2019

118 citations recorded by the source corpus.

This paper studies a deep learning (DL) framework to solve distributed non-convex constrained optimizations in wireless networks where multiple computing nodes, interconnected via backhaul links, desire to determine an efficient assignment of their states based on local observations. Two different configurations are considered: First, an infinite-capacity backhaul enables nodes to communicate in a lossless way, thereby obtaining the solution by centralized computations. Second, a practical finite-capacity backhaul leads to the deployment of distributed solvers equipped along with quantizers for communication through capacity-limited backhaul. The distributed nature and the non-convexity of the optimizations render the identification of the solution unwieldy. To handle them, deep neural networks (DNNs) are introduced to approximate an unknown computation for the solution accurately. In consequence, the original problems are transformed to training tasks of the DNNs subject to non-convex constraints where existing DL libraries fail to extend straightforwardly. A constrained training strategy is developed based on the primal-dual method. For distributed implementation, a novel binarization technique at the output layer is developed for quantization at each node. Our proposed distributed DL framework is examined in various network configurations of wireless resource management. Numerical results verify the effectiveness of our proposed approach over existing optimization techniques.

Subfields: Artificial Intelligence, Computational Mechanics, Computer Networks and Communications, Optimization & Training

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Citations in the corpus

Federated Learning With Differential Privacy: Algorithms and Performance Analysis (2019, background): “At the same time, the rapid growth of Internet-of-Things (IoT) applications calls for data mining and learning securely and reliably in distributed systems [1]–[3].”
Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing (2022, unknown): “A general framework of DL integrated optimization was proposed to solve nonconvex problems in wireless resource management [183].”
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, methodology): “Such a distributed optimization algorithm can be further learned via the distributed DL techniques [61].”
A Tutorial on Ultrareliable and Low-Latency Communications in 6G: Integrating Domain Knowledge Into Deep Learning (2021, background): “As such, the objective function is minimized, and the constraints are satisﬁed [120].”