STT-GS: Sample-Then-Transmit Edge Gaussian Splatting with Joint Client Selection and Power Control

• URL: http://arxiv.org/abs/2510.13186v1
• Date: Wed, 15 Oct 2025 06:20:47 GMT
• Title: STT-GS: Sample-Then-Transmit Edge Gaussian Splatting with Joint Client Selection and Power Control
• Authors: Zhen Li, Xibin Jin, Guoliang Li, Shuai Wang, Miaowen Wen, Huseyin Arslan, Derrick Wing Kwan Ng, Chengzhong Xu,
• Abstract summary: Edge Gaussian splatting (EGS) aggregates data from distributed clients and trains a global GS model at the edge server.<n>This paper formulates a novel GS-oriented objective function that distinguishes the view contributions of different clients.<n>It is found that the GS-oriented objective can be accurately predicted with low sampling ratios.
• Score: 77.56170394100022
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Edge Gaussian splatting (EGS), which aggregates data from distributed clients and trains a global GS model at the edge server, is an emerging paradigm for scene reconstruction. Unlike traditional edge resource management methods that emphasize communication throughput or general-purpose learning performance, EGS explicitly aims to maximize the GS qualities, rendering existing approaches inapplicable. To address this problem, this paper formulates a novel GS-oriented objective function that distinguishes the heterogeneous view contributions of different clients. However, evaluating this function in turn requires clients' images, leading to a causality dilemma. To this end, this paper further proposes a sample-then-transmit EGS (or STT-GS for short) strategy, which first samples a subset of images as pilot data from each client for loss prediction. Based on the first-stage evaluation, communication resources are then prioritized towards more valuable clients. To achieve efficient sampling, a feature-domain clustering (FDC) scheme is proposed to select the most representative data and pilot transmission time minimization (PTTM) is adopted to reduce the pilot overhead.Subsequently, we develop a joint client selection and power control (JCSPC) framework to maximize the GS-oriented function under communication resource constraints. Despite the nonconvexity of the problem, we propose a low-complexity efficient solution based on the penalty alternating majorization minimization (PAMM) algorithm. Experiments unveil that the proposed scheme significantly outperforms existing benchmarks on real-world datasets. It is found that the GS-oriented objective can be accurately predicted with low sampling ratios (e.g.,10%), and our method achieves an excellent tradeoff between view contributions and communication costs.

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