Distributed Optimization for Machine Learning on Decentralized Data and Features

在数据量和模型复杂性快速增长的情况下，扩大机器学习的需求引发了人们对开发分布式机器学习系统以及并行优化算法的广泛兴趣。大多数现有的研究都集中在以数据并行的方式在紧密耦合的机器集群之间划分计算，以处理大量的训练样本，或者以模型并行的方式处理大型模型，如深度神经网络。相比之下，该研究计划的目标是在用于机器学习的数据集（特征，样本，标签或它们的组合）本质上是分散的并且由多个参与者/域拥有时收集见解并构建模型。我们的长期愿景是设计可靠的分布式算法和系统，这些算法和系统可以从分散的数据中构建模型，而无需让参与者彼此共享原始数据或将原始数据共享到中心站点。通过有效地利用来自其他领域的数据，每个参与者都有望增强其预测能力，而仅基于其本地数据构建的模型，如果所有数据都集中收集，则以分散方式构建的联合模型有望接近和近似全局模型。同时，各方之间的模型参数的共享也应该最小化，以保护隐私和减少通信开销。为了实现这些目标，我们将引入通用复合模型结构，这些结构可以从不同去中心化场景中的数据中共同获得见解，包括按特征、按样本、按标签或按它们的组合去中心化。我们将设计理论启发的分布式优化算法来解决这些问题，开发有效的通信压缩技术来减少开销，并研究具体应用的实现问题。具体来说，我们的算法将受到ADMM，随机梯度下降（SGD）和近端SGD在异步和块设置中的收敛性的最新进展的启发。我们的通信压缩技术将受到抑制优化目标函数的平坦区域中的模型参数传输的机会的启发，而现有文献主要考虑梯度的重要性过滤器。 最后，我们将使用所提出的模型架构和算法来解决跨域推荐系统、多任务自然语言理解和协作移动的边缘计算中的各种实际应用。我们将根据每个问题固有的数据分散模式设计特定的模型组合和分解结构以及分布式算法。

The need to scale up machine learning, in the presence of a rapid growth in both data volumes and model complexity, has sparked broad interests in developing distributed machine learning systems as well as parallel optimization algorithms. Most existing studies focus on partitioning computation among a tightly coupled cluster of machines either in a data-parallel fashion, to deal with large amounts of training samples, or a model-parallel fashion, to deal with large models, such as deep neural networks. In contrast, the goal of this research program is to glean insights and build models when the dataset (features, samples, labels or a combination of them) used for machine learning is inherently decentralized and owned by multiple participants/domains. Our long-term vision is to design reliable distributed algorithms and systems that can build models from decentralized data, without letting participants share original data with each other or to a central site. By effectively leveraging data from other domains, each participant is expected to enhance its predicting power over a model built only based on its local data, while the joint model built in a decentralized way is expected to approach and approximate the global model if all data were collected centrally. In the meantime, the sharing of model parameters among parties should also be minimized to preserve privacy and reduce communication overhead. Toward these objectives, we will introduce generic composite model structures that can jointly reap insights from data in different decentralization scenarios, including decentralization by features, by samples, by labels or decentralization by a combination of them. We will design theoretically inspired distributed optimization algorithms to solve these problems, develop effective communication compression techniques to reduce overhead, and also study implementation issues for specific applications. Specifically, our algorithms will be inspired by the recent advancements in the convergence of ADMM, stochastic gradient descent (SGD) and proximal SGD in an asynchronous and blockwise setting. Our communication compression techniques will be inspired by the opportunity to suppress model parameter transfers in flat regions of the optimization objective function, whereas existing literature mainly considers significance filters for gradients.  Finally, we will use the proposed model architectures and algorithms to solve various real-world applications in cross-domain recommender systems, multitasked natural language understanding and collaborative mobile edge computing. We will design specific model composition and decomposition structures as well as distributed algorithms based on the data decentralization pattern inherent in each problem.