The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems

TL;DR

The paper introduces OARF, a comprehensive benchmark suite for federated learning that uses realistic datasets across various data types, enabling better evaluation of system performance and research opportunities.

Contribution

It presents a new benchmark suite, OARF, with diverse, realistic datasets and reference implementations for evaluating federated learning systems.

Findings

Federated learning can significantly increase end-to-end throughput.

OARF covers diverse data sizes, distributions, and tasks.

The benchmark facilitates future research in federated learning.

Abstract

This paper presents and characterizes an Open Application Repository for Federated Learning (OARF), a benchmark suite for federated machine learning systems. Previously available benchmarks for federated learning have focused mainly on synthetic datasets and use a limited number of applications. OARF mimics more realistic application scenarios with publicly available data sets as different data silos in image, text and structured data. Our characterization shows that the benchmark suite is diverse in data size, distribution, feature distribution and learning task complexity. The extensive evaluations with reference implementations show the future research opportunities for important aspects of federated learning systems. We have developed reference implementations, and evaluated the important aspects of federated learning, including model accuracy, communication cost, throughput and convergence time. Through these evaluations, we discovered some interesting findings such as federated learning can effectively increase end-to-end throughput.