标题： 分布式学习与推理系统：一种网络视角 显示英文标题

标题： Distributed Learning and Inference Systems: A Networking Perspective

摘要： 机器学习模型在各种任务中已经实现了，甚至在某些情况下超越了人类水平的性能，主要是通过静态模型的集中训练以及使用存储在集中式云中的大型模型进行推理。 然而，这种集中式方法有几个缺点，包括隐私问题、高存储需求、单点故障以及显著的计算需求。 这些挑战促使人们开发用于人工智能训练和推理的替代性去中心化和分布式方法。 分布引入了额外的复杂性，因为它需要管理多个移动部件。 为了解决这些复杂性并填补分布式人工智能系统开发中的空白，本文提出了一种新的框架，即数据和动态感知的推理和训练网络（DA-ITN）。 探讨了DA-ITN的不同组件及其功能，并突出了相关挑战和研究领域。 显示英文摘要

摘要： Machine learning models have achieved, and in some cases surpassed, human-level performance in various tasks, mainly through centralized training of static models and the use of large models stored in centralized clouds for inference. However, this centralized approach has several drawbacks, including privacy concerns, high storage demands, a single point of failure, and significant computing requirements. These challenges have driven interest in developing alternative decentralized and distributed methods for AI training and inference. Distribution introduces additional complexity, as it requires managing multiple moving parts. To address these complexities and fill a gap in the development of distributed AI systems, this work proposes a novel framework, Data and Dynamics-Aware Inference and Training Networks (DA-ITN). The different components of DA-ITN and their functions are explored, and the associated challenges and research areas are highlighted.