Configuration-driven framework simplifies reproducible machine learning experiments for radio localization

Researchers working with machine learning face a persistent problem: running rigorous, reproducible experiments requires juggling separate tools for setup, execution, version control, and evaluation. LOCALIZE attempts to streamline this workflow by placing configuration at its core rather than code.

The Core Design#

The framework shifts experiment design to human-readable configuration files, which spell out exactly what each stage of a research pipeline should do, what data it consumes, and what results it produces. A workflow orchestrator then runs each isolated stage in sequence, with explicit inputs and outputs tracked throughout. This approach automatically captures and versions code, datasets, configurations, environment specifications, and generated artifacts together—eliminating the common research pitfall of losing track of which code produced which results.

What LOCALIZE Provides#

The system comes preloaded with several conveniences for radio-localization research: ready-to-use datasets, modular processing stages, established model-development routines, and experiment templates that researchers can adapt. Yet it preserves direct access to the underlying computational pipeline for those who need to extend or modify the framework.

Performance and Usability#

A comparative evaluation tested LOCALIZE against standard Jupyter notebooks and Kedro (a popular ML pipeline tool). Within localization workflows, LOCALIZE required fewer code edits when making changes that fall within its localization-specific tooling. Computational overhead remained competitive—wall-clock time and peak memory usage stayed roughly in line with the alternatives. In scaling tests using 1x, 5x, and 10x dataset sizes, total CPU and wall time grew sublinearly, suggesting the approach handles larger workloads efficiently.

Broader Impact#

By making configuration the default rather than an afterthought, the framework encourages researchers to specify experiments rigorously from the start while preserving the flexibility for custom work downstream. The design principle—application-specific ML frameworks built on configuration—could extend beyond radio localization to other research domains facing similar reproducibility challenges.