Decentralized Edge Learning: Towards Decentralized Learning Paradigms for the Network Edge

Introduction

As the Internet’s edge has began to regain prominence in response to unprecedented advances in computing, storage, and wireless communication technology,  wireless edge networks have grown increasingly more complex, not only due to the sheer number of connected devices, but also due to the heterogeneity of those devices and the network technologies used to interconnect them. To satisfy the needs of emerging edge applications and the enormous amount of data they generate, computational resources have moved closer to the network edge, in a paradigm termed edge computing. At these same time, the pervasive growth of machine learning has not only benefitted from and encouraged this abundance of data but has also driven the development of applications on the network edge. This symbiotic relationship between networking and machine learning has enabled entirely new application paradigms, including autonomous vehicles, connected health services, and smart cities, while also introducing surfacing issues of security, privacy, resource utilization, and equity.

DEL, or Decentralized Edge Learning, aims to be one possible solution to these issues. In this research, we develop decentralized machine learning strategies to ensure proper privacy and security while also considering the heterogeneous networks these strategies will run on to ensure optimal network resource utilization and system performance. We explore various decentralized learning methods, including federated and gossip learning strategies, and understand the effect of their behavior from the networking perspective. We strive for tightly integrated machine learning and networking systems to optimize these methods. Our ultimate goal with this research is to open the door to more pervasive machine learning applications to be used in spaces they haven’t before, while still guaranteeing the safety of the end-users and the high efficiency of the computing and networking resources.

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Harikrishna Kuttivelil