提出新型分布式学习推理框架，解决中心化模型的隐私与可靠性问题。
Distributed Learning and Inference Systems: A Networking Perspective
- 设计数据与动态感知的分布式训练推理网络
- 支持多节点协同，降低存储与计算开销
- 适合隐私敏感、资源受限的AI部署场景
机器学习模型在诸多任务中已达到甚至超越人类水平，主要依赖于静态模型的集中式训练及在中心云中存储的大规模模型进行推理。然而，这种集中式方法存在隐私风险、高存储需求、单点故障和巨大算力要求等缺陷。这些挑战促使人们探索替代的去中心化与分布式AI训练与推理方法。分布式系统引入了额外复杂性，需协调多个组件。为应对这些挑战并填补分布式AI系统开发中的空白，本文提出一种新型框架——数据与动态感知的推理与训练网络（DA-ITN）。本文探讨了该框架各组件及其功能，并揭示了相关挑战与研究方向。
原文摘要 · Abstract (English)
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.
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