本期特刊旨在介绍面向人工智能物联网 (AIoT) 的分布式机器学习领域的最新进展,重点关注可信赖、隐私增强、安全和稳健的学习框架。内容涵盖学习范式、理论基础、算法、系统架构以及在数据异构、通信受限、资源有限和对抗性环境下实现智能物联网的实际应用。
This special issue aims to present recent advances in distributed machine learning for AIoT, with a strong emphasis on trustworthy, privacy-enhanced, secure, and robust learning frameworks. The focus spans learning paradigms, theoretical foundations, algorithms, system architectures, and real-world applications that enable intelligent IoT under data heterogeneity, communication constraints, limited resources, and adversarial environments.