FedEval: A Holistic Evaluation Framework for Federated Learning

TL;DR

This paper introduces FedEval, a comprehensive evaluation framework for federated learning that standardizes metrics and settings, enabling fair comparison of seven state-of-the-art algorithms across privacy, robustness, effectiveness, and efficiency.

Contribution

The paper proposes a holistic evaluation framework and platform for federated learning, addressing inconsistencies in prior evaluations and providing a detailed benchmarking study.

Findings

Identified strengths and weaknesses of seven FL algorithms.

Provided practical recommendations for algorithm selection.

Suggested future research directions in FL evaluation.

Abstract

Federated Learning (FL) has been widely accepted as the solution for privacy-preserving machine learning without collecting raw data. While new technologies proposed in the past few years do evolve the FL area, unfortunately, the evaluation results presented in these works fall short in integrity and are hardly comparable because of the inconsistent evaluation metrics and experimental settings. In this paper, we propose a holistic evaluation framework for FL called FedEval, and present a benchmarking study on seven state-of-the-art FL algorithms. Specifically, we first introduce the core evaluation taxonomy model, called FedEval-Core, which covers four essential evaluation aspects for FL: Privacy, Robustness, Effectiveness, and Efficiency, with various well-defined metrics and experimental settings. Based on the FedEval-Core, we further develop an FL evaluation platform with standardized evaluation settings and easy-to-use interfaces. We then provide an in-depth benchmarking study between the seven well-known FL algorithms, including FedSGD, FedAvg, FedProx, FedOpt, FedSTC, SecAgg, and HEAgg. We comprehensively analyze the advantages and disadvantages of these algorithms and further identify the suitable practical scenarios for different algorithms, which is rarely done by prior work. Lastly, we excavate a set of take-away insights and future research directions, which are very helpful for researchers in the FL area.