Serving text-to-image (T2I) generation on edge servers can reduce the serving latency and enhance the preservation of user privacy. Currently, mainstream T2I models generate images iteratively, and the more iterations, usually the higher the generation quality, but the longer the generation latency. Existing T2I servin...
Sen Dong, Ke Cheng, Ling-Kun Meng et al.· IEEE Transactions on Mobile...· 0 citations
In vehicular edge computing (VEC), existing task offloading approaches enable mobile vehicles (MVs) to offer ultra-low latency services for computation-intensive tasks. However, centralized offloading strategies are impractical due to MVs’ limited communication range and high mobility, requiring autonomous cooperation...
Zheng-Wei Gao, Chang-Mao Wu, Lei Yang et al.· IEEE Transactions on Mobile...· 0 citations
The integrated space-terrestrial network is playing an increasingly crucial role and is expected to become an indispensable component of future communication systems. As the demand for user terminals surges, task congestion issues are becoming more prominent, gradually emerging as a key factor affecting service quality...
Li-Jun Huo, Yi-Bing Liu, Xiao-Zhou Zhu et al.· IEEE Transactions on Mobile...· 0 citations
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
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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
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
For decades, a fundamental question about the difficulty of computing in high-dimensional spaces has resisted proof. New research resolves this question, removing a major theoretical obstacle to understanding the efficiency of randomized algorithms used across science and computing. The Kannan–Lovász–Simonovits (KLS) conjecture was proposed in 1995. It asks whether, for a broad class of high-dimensional […] The post Solving a 31-year-old grand challenge in mathematics – the KLS conjecture appear…
MIT News · Artificial Intelligence· news.mit.eduOct 15, 2026
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Microsoft Research Blog· microsoft.comOct 15, 2026
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
