Privacy-aware Adaptive Collaborative Learning Approach for Distributed Edge Networks

To facilitate the Edge AI paradigm in distributed networks, we propose novel collaborative learning methodologies for a connected network of edge nodes. Our proposed methodologies tackle the challenges in distributed learning where there are constraints on data privacy and a low degree of overlap between the classes observed by the nodes. These approaches entail sharing class distribution information between nodes, computing nodes, and class weights, training local models on each node, then aggregating the models using the determined weights. It favors nodes that have encountered unique or less common classes in their local datasets. Through a series of experiments using an activity recognition dataset, we demonstrate the effectiveness and scalability of our proposed approaches. We show the adaptive nature of the proposed approach by achieving classification accuracy above the baseline, even with little overlap between the observed classes. This study serves as a foundation for future advancements in collaborative learning on edge networks, and encourages the development of scalable solutions.