Trustworthy decentralized AI for large-scale IoT representation learning

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The project aims to address the fundamental challenges of decentralized deep learning (DDL) to make AI feasible and scalable for everyone. The proliferation of edge AI applications has been reshaping the contours of future high-performance edge computing, and DDL is a key enabler that would benefit society through distributed model training and globally shared knowledge. In the first phase of the project, we focused on developing collaborative representation learning techniques for different neural network models at the edge. We identified critical challenges and proposed new solutions to privacy protection, edge heterogeneity, and adversarial attacks and defenses in DDL. The proposed methods enable improved model generality to unseen data and robustness against adversarial attacks.