The flexible architecture of the open radio access network (O-RAN) provides effective support for the deployment of federated learning (FL). However, existing FL schemes in wireless networks often suffer from packet errors, which lead to reduced model test accuracy and increased training delay. In this paper, we propos...
Kai Qiao, Hongchao Wang, Zi-Hao Zhang et al.· IEEE Transactions on Mobile...· 0 citations
Split Learning (SL) enables collaborative model training by distributing computation between resource-constrained mobile edge devices and a central server. In this distributed framework, mobile edge devices compute the early layers of the model and transmit feature maps to the server. However, due to practical mobile w...
Wen-Xuan Zhou, Zhi-Hao Qu, Baoliu Ye et al.· IEEE Transactions on Mobile...· 0 citations
The integration of homomorphic encryption (HE) into split federated learning (SFL) offers significant security advantages by maintaining end-to-end encryption of intermediate computations. Existing SFL approaches often overlook encryption, adopting fixed machine learning (ML) model partitioning strategies without consi...
Penghui Chen, Wenhao Fan, Guan Wang et al.· IEEE Transactions on Mobile...· 0 citations
In 6G multiple uncrewed aerial vehicles (UAVs) cooperative edge computing networks, strongly coupled system states and limited single-UAV observability lead to inefficient resource management and difficulty in guaranteeing Quality of Service (QoS). To address these issues, we propose a QoS-aware resource allocation met...
Ya-Qing Wang, Lun Tang, Wei-Li Wang et al.· IEEE Transactions on Mobile...· 0 citations
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Today’s networks include decentralized intelligence, scattered over multiple computing facilities, for the execution of computation-intensive tasks that are necessary for both network management and service provision to end users. This raises a number of questions regarding task allocation, in terms of both the task pl...
V. Mancuso, P. Castagno, Leonardo Badia et al.· IEEE Transactions on Mobile...· 1 citation
Mobile edge computing effectively reduces delay and improves the system efficiency by offloading computing tasks. However, the standard communication channel, Orthogonal Frequency Division Multiple Access (OFDMA), is difficult to support large-scale connections in dense networks. Meanwhile, existing task offloading met...
Yang Xia, Min-Cong Chen, Qiang He et al.· IEEE Transactions on Mobile...· 0 citations
The rapid expansion of computation-intensive applications renders current mobile edge computing (MEC) frameworks inadequate for delivering high-quality computing services in environments with sparse network infrastructure. Air-based stations, represented by low-altitude unmanned aerial vehicles (UAVs) and high-altitude...
Meng Li, Hao-Yu Wan, Su-Yu Lv et al.· IEEE Transactions on Mobile...· 1 citation
Facing the rapid development of both user equipment (UE) and mobile applications, mobile edge computing (MEC) emerged as a new paradigm to furnish UE with satisfactory computational capability and task latency. Studies on MEC have put a great deal of focus on computation offloading, mainly involving multi-step task off...
Zhe-Yuan Hu, Jian-Wei Niu, Tao Ren et al.· IEEE Transactions on Mobile...· 0 citations
The rapid proliferation of Internet of Things (IoT) and the diversity of services demand for a more efficient and intelligent resource allocation framework to enhance network performance. To this end, we construct a novel satellite-terrestrial integrated network (STIN) integrating multi-access edge computing (MEC) and...
Fang-Fang Yin, Qi-Hong Liu, Mu-Lei Wu et al.· IEEE Transactions on Mobile...· 0 citations
Owing to increased power density with each newer technology node, transistors suffer from increased Self-Heating (SH) at room temperature, which is a daunting challenge for circuits and processors’ reliability. Given the increasing interest in leveraging cryogenic temperatures to enhance the performance and energy effi...
S. S. Parihar, F. Klemme, Anirban Kar et al.· IEEE transactions on compute...· 1 citation
In large areas, mobile edge computing (MEC) systems enabled by drones, also known as unmanned aerial vehicles (UAVs), can provide flexible edge computing services and facilitate low-altitude inspection. Such systems are primarily limited by the computing resources and energy of the drone, as well as their reliance on c...
W. Qi, Wei-Feng Zhong, Jia-Wen Kang et al.· IEEE Transactions on Mobile...· 1 citation
A load-aware GPU-based dynamic graph pattern matching scheme is proposed to make full use of GPU computing resources and a task overhead prediction model is proposed to guide task allocation to alleviate the load imbalance between multiple GPU devices.
Yu Zhang, Yu-Luo Guo, Fu-Bing Mao et al.· IEEE Transactions on Knowled...· 0 citations
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
