# Towards a Decentralized Collaborative Framework for Scalable Edge AI
###### Abstract  
Nowadays, Edge Intelligence has seen unprecedented growth in most of our daily life applications. Traditionally, most applications required significant efforts into data collection for data-driven analytics, raising privacy concerns. The proliferation of specialized hardware on sensors, wearable, mobile, and IoT devices has led to the growth of Edge Intelligence, which has become an integral part of the development cycle of most modern applications. However, scalability issues hinder their wide-scale adoption. We aim to focus on these challenges and propose a scalable decentralized edge intelligence framework. Therefore, we analyze and empirically evaluate the challenges of existing methods, and design an architecture that overcomes these challenges. The proposed approach is client-driven and model-centric, allowing models to be shared between entities in a scalable fashion. We conduct experiments over various benchmarks to show that the proposed approach presents an efficient alternative to the existing baseline method, and it can be a viable solution to scale edge intelligence. collaborative computing; distributed systems; data-driven intelligence; machine learning; model distillation +
Footnote †: journal:  
0000-0001-4000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-00  
model with their own private data, and leverages available communication channels to securely exchange the model parameters (or updates) to create a common (or global) model. Since AI/ML models perform better with larger training data, it is anticipated that the global model would have better generalization abilities compared to the local model of the clients [7]. Several methods have evolved under the Edge AI paradigm to accomplish decentralized learning objectives, such as 1. Federated Learning (FL) [8], which involves clients that conduct training of local models on their own devices, and then they engage in sending model updates to the server, which aggregates these models to produce a global model and takes care of coordinating the FL rounds; 2. Decentralized Learning (DL) [9], which leverages peer-to-peer coordination for the model exchange among the edge devices; and 3. Transfer Learning (TL) [10], which aims to share the knowledge between different models of the same or different tasks. These approaches are made possible by harnessing the advances in the AI/ML accelerators embedded in Edge devices [11] and the high-throughput and low-latency 5G and 6G technologies [12; 13]. Nonetheless, several obstacles arise, diminishing the efficacy of these technologies and rendering them unsuitable for large-scale decentralized Edge AI. Among the key challenges for these approaches are the lack of ability to maintain high statistical and system efficiency due to the highly heterogeneous devices, configurations, and environments, and the strict synchronization requirements. This results in models with low quality and long training time and hinders the existing approaches from scaling with a large number of learners [11; 14]. It should come as no surprise that low-quality models can be quite expensive for various companies and organizations. For example, only recently, the real estate company Zillow wrote down around USD 304M worth of inventory owing to the low accuracy of its Zestimate AI algorithm [15]. Modern data-driven intelligence needs systems that can produce accurate and timely models in a scalable manner [6; 11]. We highlight the fundamental issues existing centralized and decentralized approaches face and propose an innovative architectural design for decentralized collaborative learning. In this view, the trained AI/ML models are treated as a commodity that can be exchanged between learning entities to meet global or personal objectives. This view resembles most online delivery services like Uber (passengers) or Deliveroo (food). For example, Uber's task is merely connecting the passengers wanting to be transported from point A to point B with the appropriate drivers.