The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems
This provides a more realistic benchmark for federated learning researchers, though it is incremental as it builds on existing synthetic benchmarks.
The paper tackles the lack of realistic benchmarks in federated learning by introducing OARF, a benchmark suite using diverse public datasets, and finds that federated learning can increase end-to-end throughput.
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.