Holistic Evaluation Metrics: Use Case Sensitive Evaluation Metrics for Federated Learning

TL;DR

This paper introduces Holistic Evaluation Metrics (HEM) for federated learning, enabling comprehensive assessment tailored to specific use cases like IoT, smart devices, and institutions, considering multiple performance aspects.

Contribution

The paper proposes a novel HEM framework that integrates various metrics with use case-specific importance vectors for better FL algorithm evaluation.

Findings

HEM effectively differentiates FL algorithms based on use case priorities.

Experimental results validate HEM's ability to identify the most suitable FL algorithms.

HEM addresses limitations of single-metric evaluation in federated learning.

Abstract

A large number of federated learning (FL) algorithms have been proposed for different applications and from varying perspectives. However, the evaluation of such approaches often relies on a single metric (e.g., accuracy). Such a practice fails to account for the unique demands and diverse requirements of different use cases. Thus, how to comprehensively evaluate an FL algorithm and determine the most suitable candidate for a designated use case remains an open question. To mitigate this research gap, we introduce the Holistic Evaluation Metrics (HEM) for FL in this work. Specifically, we collectively focus on three primary use cases, which are Internet of Things (IoT), smart devices, and institutions. The evaluation metric encompasses various aspects including accuracy, convergence, computational efficiency, fairness, and personalization. We then assign a respective importance vector for each use case, reflecting their distinct performance requirements and priorities. The HEM index is finally generated by integrating these metric components with their respective importance vectors. Through evaluating different FL algorithms in these three prevalent use cases, our experimental results demonstrate that HEM can effectively assess and identify the FL algorithms best suited to particular scenarios. We anticipate this work sheds light on the evaluation process for pragmatic FL algorithms in real-world applications.