2 research outputs found
Sparse Optimization for Green Edge AI Inference
With the rapid upsurge of deep learning tasks at the network edge, effective
edge artificial intelligence (AI) inference becomes critical to provide
low-latency intelligent services for mobile users via leveraging the edge
computing capability. In such scenarios, energy efficiency becomes a primary
concern. In this paper, we present a joint inference task selection and
downlink beamforming strategy to achieve energy-efficient edge AI inference
through minimizing the overall power consumption consisting of both computation
and transmission power consumption, yielding a mixed combinatorial optimization
problem. By exploiting the inherent connections between the set of task
selection and group sparsity structural transmit beamforming vector, we
reformulate the optimization as a group sparse beamforming problem. To solve
this challenging problem, we propose a log-sum function based three-stage
approach. By adopting the log-sum function to enhance the group sparsity, a
proximal iteratively reweighted algorithm is developed. Furthermore, we
establish the global convergence analysis and provide the ergodic worst-case
convergence rate for this algorithm. Simulation results will demonstrate the
effectiveness of the proposed approach for improving energy efficiency in edge
AI inference systems.Comment: This work has been submitted to the IEEE for possible publication.
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When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Network
Ultra-dense edge computing (UDEC) has great potential, especially in the 5G
era, but it still faces challenges in its current solutions, such as the lack
of: i) efficient utilization of multiple 5G resources (e.g., computation,
communication, storage and service resources); ii) low overhead offloading
decision making and resource allocation strategies; and iii) privacy and
security protection schemes. Thus, we first propose an intelligent ultra-dense
edge computing (I-UDEC) framework, which integrates blockchain and Artificial
Intelligence (AI) into 5G ultra-dense edge computing networks. First, we show
the architecture of the framework. Then, in order to achieve real-time and low
overhead computation offloading decisions and resource allocation strategies,
we design a novel two-timescale deep reinforcement learning (\textit{2Ts-DRL})
approach, consisting of a fast-timescale and a slow-timescale learning process,
respectively. The primary objective is to minimize the total offloading delay
and network resource usage by jointly optimizing computation offloading,
resource allocation and service caching placement. We also leverage federated
learning (FL) to train the \textit{2Ts-DRL} model in a distributed manner,
aiming to protect the edge devices' data privacy. Simulation results
corroborate the effectiveness of both the \textit{2Ts-DRL} and FL in the I-UDEC
framework and prove that our proposed algorithm can reduce task execution time
up to 31.87%.Comment: Accepted by IEEE IoT