Zero-Local-Provisioning for Training (ZLP-Training): A General Framework for Collective Provisioning of Training Capabilities
Abstract
Training capabilities are commonly provisioned within individual computing devices, intelligent systems, organizational units, and model-deployment environments. Although local training can provide autonomy, low-latency adaptation, and direct control over model updates, it may also introduce duplicated computational infrastructure, redundant software components, maintenance costs, energy consumption, and underutilized capacity. In many systems, the ability to train or update a model does not necessarily need to be independently available within every individual object, provided that the required training functions can be supplied through compatible external services or collective infrastructure.
This paper proposes Zero-Local-Provisioning for Training (ZLP-Training), a domain-specific application of the Zero-Local-Provisioning (ZLP) framework. Its central principle is that an individual object may have a local training provisioning quantity of exactly zero while the relevant system-level training requirements remain satisfied through shared training infrastructure, external training services, delegated optimization, collective learning arrangements, or other permitted mechanisms. The framework distinguishes the local possession of training capabilities from the availability of trained models, model updates, and training-related services at the system level.
The paper formalizes objects, training capabilities, local training provisioning, training requirements, collective training assignments, model-state compatibility, data-access constraints, training quality, lifecycle costs, and operational feasibility. It introduces a zero-permissive optimization formulation that jointly determines which objects should retain local training capabilities and how the remaining training requirements should be fulfilled. The formulation distinguishes training compute, local training software, training data access, optimization capabilities, and model-update services rather than treating training as a single undifferentiated resource.
The framework also identifies conditions under which zero local training provisioning is feasible, including task compatibility, external capacity, data governance, model-update integrity, communication constraints, privacy, reliability, and adaptation requirements. A reference architecture, algorithmic template, application scenarios, and empirical evaluation methodology are presented. The paper does not claim that ZLP-Training is universally superior to local training, nor that collective training is a new capability in itself. Its central research question is whether explicitly permitting local training provisioning to equal zero, while jointly optimizing system-level training functionality, provides a useful and generalizable approach to training-system design.
Related
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BibTeX
@misc{zhang2026zerolocalprovisioning,
title = {{Zero-Local-Provisioning for Training (ZLP-Training): A General Framework for Collective Provisioning of Training Capabilities}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23451102},
url = {https://zenodo.org/records/23451102},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Zero-Local-Provisioning for Training (ZLP-Training): A General Framework for Collective Provisioning of Training Capabilities
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/18
PB - Zenodo
DO - 10.5281/zenodo.23451102
UR - https://zenodo.org/records/23451102
AB - Training capabilities are commonly provisioned within individual computing devices, intelligent systems, organizational units, and model-deployment environments. Although local training can provide autonomy, low-latency adaptation, and direct control over model updates, it may also introduce duplicated computational infrastructure, redundant software components, maintenance costs, energy consumption, and underutilized capacity. In many systems, the ability to train or update a model does not necessarily need to be independently available within every individual object, provided that the required training functions can be supplied through compatible external services or collective infrastructure. This paper proposes Zero-Local-Provisioning for Training (ZLP-Training), a domain-specific application of the Zero-Local-Provisioning (ZLP) framework. Its central principle is that an individual object may have a local training provisioning quantity of exactly zero while the relevant system-level training requirements remain satisfied through shared training infrastructure, external training services, delegated optimization, collective learning arrangements, or other permitted mechanisms. The framework distinguishes the local possession of training capabilities from the availability of trained models, model updates, and training-related services at the system level. The paper formalizes objects, training capabilities, local training provisioning, training requirements, collective training assignments, model-state compatibility, data-access constraints, training quality, lifecycle costs, and operational feasibility. It introduces a zero-permissive optimization formulation that jointly determines which objects should retain local training capabilities and how the remaining training requirements should be fulfilled. The formulation distinguishes training compute, local training software, training data access, optimization capabilities, and model-update services rather than treating training as a single undifferentiated resource. The framework also identifies conditions under which zero local training provisioning is feasible, including task compatibility, external capacity, data governance, model-update integrity, communication constraints, privacy, reliability, and adaptation requirements. A reference architecture, algorithmic template, application scenarios, and empirical evaluation methodology are presented. The paper does not claim that ZLP-Training is universally superior to local training, nor that collective training is a new capability in itself. Its central research question is whether explicitly permitting local training provisioning to equal zero, while jointly optimizing system-level training functionality, provides a useful and generalizable approach to training-system design.
ER -