提出X-Learning框架，用随机游走打破分布式学习的去中心化壁垒。
From Federated Learning to X-Learning: Breaking the Barriers of Decentrality Through Random Walks
- 基于图论与马尔可夫链，构建新型分布式学习架构
- 揭示去中心化学习与随机游走间的深层联系
- 适合对分布式系统与理论机制感兴趣的学者
本文提出一种名为X-Learning（XL）的新型分布式学习架构，旨在推广并拓展去中心化的概念。目标是呈现对XL的愿景，揭示其尚未探索的设计考量与自由度。为此，我们阐明了XL、图论与马尔可夫链之间直观但非平凡的联系，并提出一系列开放性研究方向，以激发进一步探索。
原文摘要 · Abstract (English)
We provide our perspective on X-Learning (XL), a novel distributed learning architecture that generalizes and extends the concept of decentralization. Our goal is to present a vision for XL, introducing its unexplored design considerations and degrees of freedom. To this end, we shed light on the intuitive yet non-trivial connections between XL, graph theory, and Markov chains. We also present a series of open research directions to stimulate further research.
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