Edge computing has gradually become a critical means of addressing the latency and bandwidth bottlenecks inherent in cloud computing. By deploying computing and storage resources at network edge nodes, edge computing is capable of providing low-latency and high-bandwidth services to Internet of Things (IoT) devices. To...
B. Su, Ren-Cong Lin, Zhenxing Li et al.· IEEE Internet of Things Jour...· 0 citations
As computing demands continue to grow, a single server is no longer sufficient to meet user requirements, leading to increasing interest in multiserver collaborative edge computing. However, load imbalance is a prevalent issue in multiserver edge computing systems, resulting in inefficient resource utilization and degr...
Jingzhe Wang, Si-yu Lin, Qingqing Pan et al.· IEEE Internet of Things Jour...· 0 citations
The rapid growth of cloud-edge collaborative computing and its increasing electricity demand are forging strong interdependencies between computing and power markets. This paper proposes a market-driven joint trading strategy that coordinates computing services and electricity procurement within a cloud-edge collaborat...
Lirong Deng, Yi Sun, Yang Fu· IEEE Transactions on Smart G...· 1 citation
Federated learning (FL) is a distributed machine learning (ML) paradigm that has been widely used to train ML models on massive amounts of data in edge computing (EC) environments. However, FL faces significant challenges from device heterogeneity, edge dynamics, and limited communication resources. To address these ch...
Junyi Deng, Jiahua Liu, Yanheng Liu et al.· IEEE Internet of Things Jour...· 0 citations
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A novel AI-driven distributed NIDS that considers the computing capabilities of resource-constrained nodes while enabling efficient learning in distributed environments is proposed and can achieve accuracy comparable to a centralized model while reducing local computational overhead and maintaining stable convergence u...
Cheolhee Park, Kyungmin Park, Jihyeon Song et al.· IEEE Internet of Things Jour...· 0 citations
This article introduces KirchhoffNet, a novel class of neural network models inspired by the principles of analog electronic circuitry, specifically Kirchhoff’s laws. KirchhoffNet operates as an analog circuit, where the network input is represented by initial node voltages, and the output corresponds to the node volta...
Su Zheng, Zhengqi Gao, Fan-Keng Sun et al.· IEEE Transactions on Compute...· 0 citations
The analysis shows that services with stringent latency deadlines and larger computing demands are more sensitive to temporal variabilities, making local execution the preferred option, and that effective service offloading must jointly consider service requirements and sources of temporal variability to guarantee dete...
K. Aghababaiyan, Javier Gozálvez, B. Coll-Perales· 0 citations
This paper presents MetaSieve, a metapath selection layer that determines which metapaths to retain and which to prune, and shows that MetaSieve consistently reduces per-epoch training time by large margins while maintaining and often improving accuracy.
Fahim Shahriar Khan, Ashraf Aboulnaga· 0 citations
This study investigated the neural and molecular bases of individual differences in adolescent MDD patients by integrating a novel edge-centric brain connectome with transcriptomic and neurotransmitter profiles, and identified two robust adolescent MDD subtypes.
Yingbo Shao, Bao-Lin Wu, Xun Zhang et al.· European Child and Adolescen...· 0 citations
This work develops a multi-agent reinforcement learning (MARL) algorithm, termed Multi-Agent Proximal Policy Optimization with Dirichlet Modeling (MAPPO-DM), which follows the centralized-training-and-decentralized-execution framework and models continuous traffic-splitting actions using a Dirichlet distribution.
Zhenyu Zhao, Tian-Kui Zhang, Xiao-Xia Xu et al.· 0 citations
This work shows that one can extract LLM assets during inference, namely embeddings, attention, and quantized MLP weights, activations, and other inference states, from localized memories and compute subcircuits from localized memories and compute subcircuits by deploying laser voltage imaging.
Dev M. Mehta, Lily Dukette, William Folan et al.· 0 citations
A real-time status monitoring and traceability system based on the edge nodes of the Internet of Things is constructed, providing a replicable technical paradigm for the digital protection of intangible cultural heritage processes.
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
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.
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