# Towards a Decentralized Collaborative Framework for Scalable Edge AI
## 5 The Proposed Architecture  
This work addresses the following central question: **What would be the architectural design for allowing scalable collaborative learning over distributed data? ** We position this work as a call for architectural designs that can:  
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* Efficiently enable various types of decentralized training between learning parties;
* Securely store a representation of the models in vaults, which ensure only authorized access to clients' models;
* Develop scalable model discovery methods by providing a distributed service to resolve clients' model requests by finding the best models matching their search criteria. Next, we will discuss a potential design for a decentralized architecture that aims to capture the essence of the aforementioned goals and objectives. The proposed architecture is presented in Figure 2. The system introduces a Model Discovery and Distillation (MDD) service that aids any party with the network in requesting and attaining knowledge within other models shared with the network. The service helps discover the requested model meeting the requirements and the distillation process with servers suited at the edge. Specifically, these servers and the target client of distillation can arrange for the distillation of the discovered knowledge to the target client's model. This process can be any form of knowledge transfer learning method [10], such as knowledge distillation [32], domain adaptation [33], mutual learning [34] fine-tuning, and personalization [35]. This process could take a few rounds of knowledge transfer until the target client's objective is met (i.e., target accuracy is achieved). The advantage of the proposed architecture is that it has no single point of control and failure (like FL), no explicit synchronization among devices (like DL), and no expensive movement of private data (like CL). This is because the MDD process is client-driven, where learners create an initial model with their local datasets and then asynchronously seek models in the network to boost the model quality via distillation. The system discovers the model for the requester without involving other learners, which helps mitigate any influence of the clients' heterogeneity. The proposed architecture may also introduce incentive mechanisms (e.g., based on monetary income or mutual interest [22]) to enable the sharing of high-quality models in the network. There are various ways to realize incentives in the MDD architecture to encourage knowledge dissemination and sharing of high-quality models [36]. For instance, deep reinforcement learning (DRL) agents could be leveraged to learn the optimal pricing strategy the MDD service uses, incentivizing the clients towards model training [37]. Similarly, clients can also share the utility of each shared model they receive to guide the system for distributing incentives [38]. The utility is calculated as the percentage of average accuracy improvement over the model trained solely on the client's data. In the proposed architecture, as shown in Figure 2, the data owners train an initial model individually or in groups (e.g., via Federated Learning). Then, the learner(s) request to store the model in private and secure model stores (or vaults). The system will evaluate the model either on a public dataset by the service or via requesting testing parties to obtain the quality metrics of the model. Whenever they require improvements on their model, the learning parties request a trained model to the discovery service specifying certain qualities of the requested model (e.g., a classifier needs to improve its accuracy to reach at least 90% of accuracy for class D). This requires novel discovery algorithms to find the best models in the network matching the requested knowledge. We provide the details of the algorithm supporting the proposed architecture in Algorithm 1.