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Review and Comparative Evaluation of Resource-Adaptive Collaborative Training for Heterogeneous Edge Devices

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  • NETL
  • NSTL
  • Assoc Computing Machinery
Growing concerns about centralized mining of personal data threatens to stifle further proliferation ofmachine learning (ML) applications. Consequently, a recent trend in ML training advocates for a paradigmshift – moving the computation of ML models from a centralized server to a federation of edge devicesowned by the users whose data is to be mined. Though such decentralization aims to alleviate concernsrelated to raw data sharing, it introduces a set of challenges due to the hardware heterogeneity among thedevices possessing the data. The heterogeneity may, in the most extreme cases, impede the participation oflow-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 MLmodel training over heterogeneous devices. However, the actual extent to which the issue is solved remainsunclear, especially as an independent investigation of the proposed methods’ performance in realisticsettings is missing. In this paper, we present a detailed survey and an evaluation of algorithms that aim toenable collaborative model training across diverse devices. We explore approaches that harness three majorstrategies for DCML, namely Knowledge Distillation, Split Learning, and Partial Training, and we conduct athorough 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, CPUutilization, network activity, and other relevant metrics, and provides guidelines for practitioners as well aspointers for future research in DCML.

Federated learningsplit learningdistributed collaborative learningubiquitous and mobile computingdevice heterogeneity

BORIS RADOVIC、MARCO CANINI、VELJKO PEJOVIC

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King Abdullah University of Science and Technology, Thuwal, Saudi Arabia and Universityof Ljubljana, Faculty of Computer and Information Science, Ljubljana, Slovenia

King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

University of Ljubljana, Faculty of Computer and Information Science, Ljubljana,Slovenia and Jozef Stefan Institute, Ljubljana, Slovenia

2025

10.1145/3708983

ACM Transactions on Modeling and Performance Evaluation of Computing Systems

ACM Transactions on Modeling and Performance Evaluation of Computing Systems

ISSN：2376-3639
年,卷(期)：2025.10(1)
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