Search · four archives
Search · four archives
11 papers · ranked by Valyu relevance
Onur Günlü, Maciej Skorski, H. Vincent Poor, Chi Wan Sung
Semantic communication frameworks aim to convey the underlying significance of data rather than reproducing it exactly, a perspective that enables substantial efficiency gains in settings constrained by latency or bandwidth. Motivated by this shift, we study the rate-distortion-perception (RDP) trade-off for image…
Arjhun Swaminathan, Anika Hannemann, Ali Burak Ünal, Nico Pfeifer + 1 more
Genome-wide association studies help uncover genetic influences on complex traits and diseases. Importantly, multi-site data collaborations enhance the statistical power of these studies but pose challenges due to the sensitivity of genomic data. Existing privacy-preserving approaches to performing multi-site…
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…
Guo-An Qi, Qi-Xin Zhang, Jingyu Kang, Tianyuan Li + 6 more
Title: Author summary For a complex trait, heritability (h 2) gives the genetic determination of its variation. Given the emergence of biobank-scale data, a more powerful method is needed to estimate h 2. Based on the framework of Haseman-Elston regression (RHE-reg), we integrate a fast randomization algorithm to…
Marius de Arruda Botelho, Cem Ata Baykara, Ali Burak Ünal, Nico Pfeifer + 2 more
Ensuring privacy in distributed machine learning while computing the Area Under the Curve (AUC) is a significant challenge because pooling sensitive test data is often not allowed. Although cryptographic methods can address some of these concerns, they may compromise either scalability or accuracy. In this paper, we…
Xiufang Sun, Ruze Zhang, Dan Li, Xuan Guang + 1 more
The problem of multi-function computation over a directed acyclic network is investigated in this paper. In such a network, a sink node is required to compute with zero error multiple vector-linear functions, where each vector-linear function has distinct inputs generated by multiple source nodes. The computing rate…
Mingcong Wu, Alessandro Giuliani
This paper investigates robust high-dimensional convoluted rank regression in distributed environments. We propose an estimation method suitable for sparse regimes, which remains effective under heavy-tailed errors and outliers, as it does not impose moment assumptions on the noise distribution. To facilitate scalable…
Jiao Xue, Chundong Wang
Introduction Federated learning (FL) is a distributed machine learning paradigm that preserves data privacy and mitigates data silos. Nevertheless, frequent communication between clients and the server often becomes a major bottleneck, restricting training efficiency and scalability. Methods To address this challenge…
Hamid Saeedi, Ali Nouruzi
This paper proposes a cooperative framework for resource allocation in multi-access edge computing (MEC) under a partial task offloading setting, addressing the joint challenges of learning performance and system efficiency in heterogeneous edge environments. In the proposed architecture, selected users act as edge…
Weifei Gan, Hongxuan Xu, Yunwei Bai, Xin Zhou + 3 more
Large multi-UAV mission systems operate over time-varying communication graphs with heterogeneous platforms, where classical distributed task assignment may incur excessive message passing and suboptimal task-resource matching. To address these challenges, this paper proposes CLAC-CBBA (Centrality-Driven and Load-Aware…
Fei Liu, ZhiLi Liu, XiaoHong Liu, Hua Zhou
Fog computing offers a decentralized paradigm to address the low-latency and energy-efficiency requirements of emerging IoT applications. However, the heterogeneity of edge nodes, the dynamic nature of workloads, and the dual need for both real-time and non-real-time scheduling introduce significant challenges in task…