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.

本文提出和描述了联邦学习开放应用程序仓库（OARF），这是一个联邦机器学习系统基准套件。 OARF 使用公开可用的数据集作为图像、文本和结构化数据中的不同数据储存库，将更真实的应用场景与以前的基准集合合并，此外通过评估重要的联邦学习方面，如模型准确性，通信成本，吞吐量和收敛时间的性能，我们发现联邦学习可以有效地增加端到端吞吐量。

OARF 基准套件：联邦学习系统的特性和影响分析

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

Recently, researchers have made significant progress combining the advances
in deep learning for learning feature representations with reinforcement
learning. Some notable examples include training agents to play Atari games
based on raw pixel data and to acquire advanced manipulation skills using raw
sensory inputs. However, it has been difficult to quantify progress in the
domain of continuous control due to the lack of a commonly adopted benchmark.
In this work, we present a benchmark suite of continuous control tasks,
including classic tasks like cart-pole swing-up, tasks with very high state and
action dimensionality such as 3D humanoid locomotion, tasks with partial
observations, and tasks with hierarchical structure. We report novel findings
based on the systematic evaluation of a range of implemented reinforcement
learning algorithms. Both the benchmark and reference implementations are
released at this https URL in order to facilitate experimental
reproducibility and to encourage adoption by other researchers.

研究人员结合深度学习和强化学习在连续控制领域缺失常用基准问题下设计并发布了一套基准问题和参考实现，经过系统评估发现一系列新的结果。