Efficient Methods for Distributed Machine Learning and Resource Management in the Internet-of-Things

Abstract

Undoubtedly, this century evolves in a world of interconnected entities, where the notion of Internet-of-Things (IoT) plays a central role in the proliferation of linked devices and objects. In this context, the present dissertation deals with large-scale networked systems including IoT that consist of heterogeneous components, and can operate in unknown environments. The focus is on the theoretical and algorithmic issues at the intersection of optimization, machine learning, and networked systems. Specifically, the research objectives and innovative claims include: (T1) Scalable distributed machine learning approaches for efficient IoT implementation; and, (T2) Enhanced resource management policies for IoT by leveraging machine learning advances. Conventional machine learning approaches require centralizing the users' data on one machine or in a data center. Considering the massive amount of IoT devices, centralized learning becomes computationally intractable, and rises serious privacy concerns. The widespread consensus today is that besides data centers at the cloud, future machine learning tasks have to be performed starting from the network edge, namely mobile devices. The first contribution offers innovative distributed learning methods tailored for heterogeneous IoT setups, and with reduced communication overhead. The resultant distributed algorithm can afford provably reduced communication complexity in distributed machine learning. From learning to control, reinforcement learning will play a critical role in many complex IoT tasks such as autonomous vehicles. In this context, the thesis introduces a distributed reinforcement learning approach featured with its high communication efficiency. Optimally allocating computing and communication resources is a crucial task in IoT. The second novelty pertains to learning-aided optimization tools tailored for resource management tasks. To date, most resource management schemes are based on a pure optimization viewpoint (e.g., the dual (sub)gradient method), which incurs suboptimal performance. From the vantage point of IoT, the idea is to leverage the abundant historical data collected by devices, and formulate the resource management problem as an empirical risk minimization task --- a central topic in machine learning research. By cross-fertilizing advances of optimization and learning theory, a learn-and-adapt resource management framework is developed. An upshot of the second part is its ability to account for the feedback-limited nature of tasks in IoT. Typically, solving resource allocation problems necessitates knowledge of the models that map a resource variable to its cost or utility. Targeting scenarios where models are not available, a model-free learning scheme is developed in this thesis, along with its bandit version. These algorithms come with provable performance guarantees, even when knowledge about the underlying systems is obtained only through repeated interactions with the environment. The overarching objective of this dissertation is to wed state-of-the-art optimization and machine learning tools with the emerging IoT paradigm, in a way that they can inspire and reinforce the development of each other, with the ultimate goal of benefiting daily life.