Why Decentralize Deep Learning?

Abstract

Deep learning, big data, IoT and blockchain are individually very important research topics of today’s technology, and their combination has the potential to generate additional synergy. Such synergy could enable decentralized and intelligent automated applications to achieve safety, security and optimize performance and economy. Deep learning, big data, IoT and blockchain all rely on infrastructure capabilities in computing and communications that are increasingly decentralized. Edge computing deployments and architectures are commencing with 5G and expected to accelerate in 6G. Existing application domains like healthcare and finance are starting to explore the integration of these technologies. Newly emerging application areas such as the metaverse may well require native support of decentralized deep learning to achieve their potential. But the path of new technology development is never smooth. New challenges have been identified and additional architectural frameworks have been developed to overcome some of these issues. Decentralizing deep learning enables increased scale for AI implementations, but also enables improvements in privacy and trustworthiness. The plethora of literature emerging on decentralized deep learning prompts the need for rationale criteria to support design decisions for implementation to utilize decentralized deep learning