Search · four archives
Search · four archives
14 papers · ranked by Valyu relevance
Haobo Jia, Zhuqing Jia, Shenghui Song
We study the problem of locally encoded secure distributed batch matrix multiplication (LESDBMM), where M pairs of sources each encode their respective batches of massive matrices and distribute the generated shares to a subset of N worker nodes. Each worker node computes a response from the received shares and sends…
Yifei Huang, Siying Luo, Bowen Zheng, Chi Wan Sung
In the traditional $(K,L,M_{T},M_{U},N)$ partially connected linear network, a central server stores a library of N files and connects to $(K+L-1)$ transmitters, each equipped with a cache of size $M_{T}$. Each user is connected to L neighboring transmitters and is equipped with a local cache of size $M_{U}$. Motivated…
Yuqi Jiang, Tianyi Mao, Jianyong Zhou, Qile Zhao + 4 more
Coded aperture X-ray computed tomography (CAXCT) measures coded X-ray projections to reconstruct the inner structure of an object. Coded apertures, which determine the point spread function, can be designed to improve the reconstruction quality, but most approaches are computationally expensive, leading to very small…
Daniele Malpetti, Marco Scutari, Francesco Gualdi, Jessica van Setten + 5 more
Federated learning leverages data across institutions to improve clinical discovery while complying with data-sharing restrictions and protecting patient privacy. This paper provides a gentle introduction to this approach in bioinformatics, and is the first to review key applications in proteomics, genome-wide…
Mohamad Hayek, Martin Golasowski, Stephan Hachinger, Rubén J. García-Hernández + 8 more
Modern data-management frameworks promise a flexible and efficient management of data and metadata across storage backends. However, such claims need to be put to a meaningful test in daily practice. We conjecture that such frameworks should be fit to construct a data backend for workflows which use geographically…
Jun Wang, Xianghua Chen, Xing Cheng, Jiantong Zhang + 3 more
In edge computing scenarios, the data generated by distributed devices is characterized by its dispersion, heterogeneity, and privacy sensitivity, posing significant challenges to federated clustering, including high communication overhead, difficulty in adapting to non-IID data, and significant privacy leakage risks.…
Huzaif Khan, Rahul Kavati, Sriven Srilakshmi Pulkaram, Ali Jalooli + 4 more
The widespread use of wearable Internet of Things (IoT) devices has transformed modern healthcare through the real-time monitoring of physiological signals. However, real- time responsiveness and data privacy are big challenges. Federated Learning (FL) keeps direct data exposure to a minimum but is susceptible to…
Emmanuella Adu, Yeongmuk Lee, Jihwan Moon, Sooyoung Jang + 3 more
Multi-access edge computing (MEC) has been widely recognized as a promising solution for alleviating the computational burden on edge devices, particularly in supporting fast and real-time processing of resource-intensive applications. In this paper, we propose a decentralized offloading decision strategy based on…
Yueming Qi, Yu Du, Yijun Guo, Jianjun Hao + 2 more
Leveraging non-terrestrial networks for edge computing is crucial for the development of 6G, the Internet of Things, and ubiquitous digitalization. In such scenarios, diverse tasks often exhibit continuously distributed attributes, while existing research predominantly relies on qualitative thresholds for task…
Bahman Arasteh, Seyed Salar Sefati, Huseyin Kusetogullari, Farzad Kiani + 3 more
Efficient task scheduling remains a key challenge in High-Performance Computing and Internet of Things (IoT) systems, where the sequential execution of nested loops often limits parallelism. This paper proposes a hybrid approach that dynamically parallelizes nested loops in heterogeneous IoT environments. The suggested…
Lingyu Zhao, Xiaorong Zhu, Jianhong Cai, Jingjing Wang
With the rapid expansion of data scale, compute-intensive tasks will become a core application of 6G networks. As Unmanned Aerial Vehicle (UAV) technology advances, UAVs can assist in task offloading for mobile edge computing by collaborating to overcome individual UAV limitations in battery life and computational…
Panagiotis K. Gkonis, Anastasios Giannopoulos, Nikolaos Nomikos, Lambros Sarakis + 4 more
The goal of the study presented in this work is to analyze all recent advances in the context of the computing continuum and meta-operating systems (meta-OSs). The term continuum includes a variety of diverse hardware and computing elements, as well as network protocols, ranging from lightweight Internet of Things…
Qizheng Sun, Caili Guo, Meiyi Zhu, Yang Yang + 4 more
U-shaped Split Federated Learning (U-SFL) is a promising paradigm for distributed image coding, offering parallel training capabilities and privacy preservation while mitigating computational burdens on edge devices. However, the frequent bidirectional transmission of intermediate features between dual-split points…
Kees Schouhamer Immink, Jos H. Weber, Tuan Thanh Nguyen, Kui Cai + 2 more
The design of low-complexity and efficient constrained codes has been a major research item for many years. This paper reports on a versatile method named concatenated constrained codes for designing efficient fixed-length constrained codes with small complexity. A concatenated constrained code comprises two (or more)…