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This paper presents and characterizes an Open Application Repository forFederated Learning (OARF), a benchmark suite for federated machine learningsystems. Previously available benchmarks for federated learning have focusedmainly on synthetic datasets and use a limited number of applications. OARFmimics more realistic application scenarios with publicly available data setsas different data silos in image, text and structured data. Ourcharacterization shows that the benchmark suite is diverse in data size,distribution, feature distribution and learning task complexity. The extensiveevaluations with reference implementations show the future researchopportunities for important aspects of federated learning systems. We havedeveloped reference implementations, and evaluated the important aspects offederated learning, including model accuracy, communication cost, throughputand convergence time. Through these evaluations, we discovered some interestingfindings such as federated learning can effectively increase end-to-endthroughput.

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