20.500.12556/RUL-166789 Review and comparative evaluation of resource-adaptive collaborative training for heterogeneous edge devices Growing concerns about centralized mining of personal data threatens to stifle further proliferation of machine learning (ML) applications. Consequently, a recent trend in ML training advocates for a paradigm shift – moving the computation of ML models from a centralized server to a federation of edge devices owned by the users whose data is to be mined. Though such decentralization aims to alleviate concerns related to raw data sharing, it introduces a set of challenges due to the hardware heterogeneity among the devices possessing the data. The heterogeneity may, in the most extreme cases, impede the participation of low-end devices in the training or even prevent the deployment of the ML model to such devices. Recent research in distributed collaborative machine learning (DCML) promises to address the issue of ML model training over heterogeneous devices. However, the actual extent to which the issue is solved remains unclear, especially as an independent investigation of the proposed methods’ performance in realistic settings is missing. In this paper, we present a detailed survey and an evaluation of algorithms that aim to enable collaborative model training across diverse devices. We explore approaches that harness three major strategies for DCML, namely Knowledge Distillation, Split Learning, and Partial Training, and we conduct a thorough experimental evaluation of these approaches on a real-world testbed of 14 heterogeneous devices. Our analysis compares algorithms based on the resulting model accuracy, memory consumption, CPU utilization, network activity, and other relevant metrics, and provides guidelines for practitioners as well as pointers for future research in DCML. federated learning split learning distributed collaborative learning ubiquitous and mobile computing device heterogeneity zvezno učenje porazdeljeno učenje vseprisotno računalništvo mobilno računalništvo true false true Angleški jezik Slovenski jezik Članek v reviji 2025-01-24 13:43:53 2025-01-24 13:43:54 2025-09-27 04:27:47 0000-00-00 00:00:00 2025 0 0 35 str. no. 1, art. 4 Vol. ǂ10 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004 2376-3639 10.1145/3708983 221443843 RAZ_Radovic_Boris_2025.pdf RAZ_Radovic_Boris_2025.pdf 1 2F04B57DBBF3A2B6ADF278C437DE4E48 90782c1ea51f231a91e795d7b3cf7622083d1cd8b21a447028234917e28c9e08 f0080f07-9ab3-11f0-9328-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=218799 https://dl.acm.org/doi/10.1145/3708983 1 1344d155-da50-11ef-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=198694 Fakulteta za računalništvo in informatiko 0 0 0