Actions at the Edge: Jointly Optimizing the Resources in Multi-access Edge Computing
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Abstract (arXiv)
Multi-access edge computing (MEC) is an emerging paradigm that pushes resources for sensing, communications, computing, storage and intelligence (SCCSI) to the premises closer to the end users, i.e., the edge, so that they could leverage the nearby rich resources to improve their quality of experience (QoE). Due to the growing emerging applications targeting at intelligentizing life-sustaining cyber-physical systems, this paradigm has become a hot research topic, particularly when MEC is utilized to provide edge intelligence and real-time processing and control. This article is to elaborate the research issues along this line, including basic concepts and performance metrics, killer applications, architectural design, modeling approaches and solutions, and future research directions. It is hoped that this article provides a quick introduction to this fruitful research area particularly for beginning researchers.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Apr 2022.
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Citing papers per year: 2023: 2; 2024: 3; 2025: 2; 2026: 1.
8 papers.
- 2023Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networkscited by 95
- 2024AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networkscited by 64
- 2024ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devicescited by 16
- 2025HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systemscited by 15
- 2023Joint Service Caching, Communication and Computing Resource Allocation in Collaborative MEC Systems: A DRL-based Two-timescale Approachcited by 2
- 2024dRG-MEC: Decentralized Reinforced Green Offloading for MEC-enabled Cloud Networkcited by 0
- 2025Communication and Computation Efficient Split Federated Learning in O-RANcited by 0
- 2026GAPSL: A Gradient-Aligned Parallel Split Learning over Data-Heterogeneous Edge Computing Systemscited by 0
- 2026GAPSL: A Gradient-Aligned Parallel Split Learning over Data-Heterogeneous Edge Computing Systems19 Mar 2026
- 2025Communication and Computation Efficient Split Federated Learning in O-RAN4 Aug 2025
- 2025HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems10 Jun 2025
- 2024AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks19 Mar 2024
- 2024ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devices24 Feb 2024
- 2024dRG-MEC: Decentralized Reinforced Green Offloading for MEC-enabled Cloud Network10 Jan 2024
- 2023Joint Service Caching, Communication and Computing Resource Allocation in Collaborative MEC Systems: A DRL-based Two-timescale Approach19 Jul 2023
- 2023Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks26 Mar 2023