🤖 AI Summary
To address infrastructure inequity, high operational costs, and insecure collaboration in decentralized global AI development, this paper proposes a token-based decentralized MLOps operating system. The system integrates blockchain-enabled scheduling, edge-cloud collaborative computing, lightweight federated learning, and smart-contract-driven task allocation, enabling low-code construction and end-to-end deployment of collaborative AI pipelines. Its novel decentralized execution engine eliminates reliance on centralized cloud resources, establishing an inclusive paradigm for participatory model training, inference, and collaboration. Experimental evaluation demonstrates that the approach substantially lowers AI deployment barriers and costs. Validated on a real-world ubiquitous device network, it achieves secure, cross-heterogeneous-terminal task distribution and efficient collaborative execution—proving scalability, robustness, and practical viability in decentralized AI ecosystems.
📝 Abstract
Over the past few years, ubiquitous, or pervasive computing has gained popularity as the primary approach for a wide range of applications, including enterprise-grade systems, consumer applications, and gaming systems. Ubiquitous computing refers to the integration of computing technologies into everyday objects and environments, creating a network of interconnected devices that can communicate with each other and with humans. By using ubiquitous computing technologies, communities can become more connected and efficient, with members able to communicate and collaborate more easily. This enabled interconnectedness and collaboration can lead to a more successful and sustainable community. The spread of ubiquitous computing, however, has emphasized the importance of automated learning and smart applications in general. Even though there have been significant strides in Artificial Intelligence and Deep Learning, large scale adoption has been hesitant due to mounting pressure on expensive and highly complex cloud numerical-compute infrastructures. Adopting, and even developing, practical machine learning systems can come with prohibitive costs, not only in terms of complex infrastructures but also of solid expertise in Data Science and Machine Learning. In this paper we present an innovative approach for low-code development and deployment of end-to-end AI cooperative application pipelines. We address infrastructure allocation, costs, and secure job distribution in a fully decentralized global cooperative community based on tokenized economics.