Serverless edge computing (SEC) is an emerging paradigm for delivering low-latency and auto-scaling services on resource-limited edges. However, when applied to DNN inference applications, SEC suffers from significant cold-start overhead, because large model parameters need to be fetched from remote model registries be...
Zhao-Wu Huang, Fang Dong, Xiao-Lin Guo et al.· IEEE Transactions on Mobile...· 0 citations
Deep neural networks (DNNs) are increasingly being employed in delay-sensitive edge applications such as autonomous driving, industrial automation, and extended reality. However, due to the use of complex computing hardware and algorithms, the execution time of a DNN is stochastic and follows a distribution usually wit...
Zhuo-Ran Chen, Jia-Le Chen, Rui Tan et al.· IEEE Transactions on Mobile...· 0 citations
This paper studies task offloading in infrastructure-free mobile edge computing (MEC), where end devices must autonomously discover candidate servers and make offloading decisions under dynamic connectivity and time-varying computing resources. However, the lack of centralized coordination incurs high overhead to acqui...
Xiang Li, Wen-Bin Xu, Shan Zhang et al.· IEEE Transactions on Mobile...· 0 citations
Multi-access edge computing (MEC) enables low-latency computing services, yet third-party resource providers integrated into MEC systems may oversell their computing capacity, severely degrading task scheduling performance. To address this, we propose an overselling-aware task scheduling scheme with deep reinforcement...
Sai-Qin Long, Jiang-Hua Qian, Jian-Hui Wang et al.· IEEE Transactions on Mobile...· 1 citation
Reach audiences
Advertise in front of researchers, engineers, and readers.
We innovatively propose that the modern edge computing infrastructure equipped with battery energy storage can actively operate as a Virtual Power Plant (VPP) to support grid stability through real-time energy dispatch. However, the edge infrastructure does not directly control the workload of the edge AI services that...
Heng-Di Wang, Lei Jiao, Kong-Lin Zhu et al.· IEEE Transactions on Mobile...· 1 citation
Computation-intensive and latency-sensitive applications often exceed the processing capabilities of User Devices (UDs). Unmanned Aerial Vehicles (UAVs) can assist Mobile Edge Computing (MEC) by enabling task offloading, thereby reducing service latency. However, the limited onboard energy of UAVs restricts continuous...
Ya-Di He, Zhi-Peng Cao, Jia Xu et al.· IEEE Transactions on Mobile...· 0 citations
Unmanned aerial vehicle (UAV)-aided vehicular edge computing (VEC) faces dual challenges of security against eavesdropping and covertness against detection, especially in high-mobility vehicle-to-everything scenarios. This paper presents a UAV-aided multi-vehicle secure-covert edge-offloading system that incorporates c...
Tao Ren, Xue-Yan Cao, Jun Cui et al.· IEEE Transactions on Mobile...· 1 citation
Federated Learning (FL) has emerged as a promising paradigm for fine-tuning large-scale Foundation Models (FMs) in distributed environments while preserving data privacy. However, efficiently adapting FMs in FL remains challenging, especially in mobile edge computing scenarios where devices are resource-constrained and...
Ya-Lan Jiang, Bin Song· IEEE Transactions on Mobile...· 0 citations
Deploying federated learning (FL) in mobile edge computing (MEC) networks enables collaborative model training while preserving the privacy of raw data. However, due to system heterogeneity, statistical heterogeneity of mobile clients (MCs), and client dropout, optimizing bandwidth allocation and fine-tuning the global...
Jian Tang, Lu-Xi Cheng, Xiu-Hua Li et al.· IEEE Transactions on Mobile...· 0 citations
Hierarchical Federated Learning (HFL) has emerged as a promising evolution of Federated Learning (FL) where a shared model is learned collaboratively via hierarchical aggregation, and it requires federated unlearning to protect users’ right to be forgotten in the training process. However, most existing designs neglect...
Ying Qian, Lian-Bo Ma, Guo Yu et al.· IEEE Transactions on Mobile...· 0 citations
Vehicular edge computing (VEC) plays a pivotal role in enabling cooperative perception for connected autonomous vehicles (CAVs), providing comprehensive environmental awareness for safe vehicle control and road safety. However, collaborative perception in VEC faces significant challenges arising from stringent bandwidt...
Wen-Qiang Ma, Wen Sun, Jian-Hua He et al.· IEEE Transactions on Mobile...· 0 citations
We present a scalable method for computing persistence pairing graphs from large cubical filtrations and use it to test whether graph organization among persistent classes contains information beyond persistence intervals alone. The method cancels equal-value pairs before global reduction, constructs the compressed com...
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 2, 2026
