Trustworthy decentralized AI for large-scale IoT representation learning

该项目旨在解决去中心化深度学习（Decentralized Deep Learning，简称DAI）的基本挑战，使AI对每个人都可行且可扩展。边缘人工智能应用的激增正在重塑未来高性能边缘计算的轮廓，而边缘计算是一个关键的推动因素，它将通过分布式模型训练和全球共享知识使社会受益。在项目的第一阶段，我们专注于为边缘的不同神经网络模型开发协作表示学习技术。我们确定了关键的挑战，并提出了新的解决方案，以保护隐私，边缘异质性，对抗性攻击和防御。所提出的方法能够提高模型对未知数据的通用性和对对抗性攻击的鲁棒性。

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.