Data within a specific context gains deeper significance beyond its isolated interpretation. In distributed systems, interdependent data sources reveal hidden relationships and latent structures, representing valuable information for many applications. This paper introduces Osmotic Learning (OSM-L), a self-supervised distributed learning paradigm designed to uncover higher-level latent knowledge from distributed data. The core of OSM-L is osmosis, a process that synthesizes dense and compact representation by extracting contextual information, eliminating the need for raw data exchange between distributed entities. OSM-L iteratively aligns local data representations, enabling information diffusion and convergence into a dynamic equilibrium that captures contextual patterns. During training, it also identifies correlated data groups, functioning as a decentralized clustering mechanism. Experimental results confirm OSM-L's convergence and representation capabilities on structured datasets, achieving over 0.99 accuracy in local information alignment while preserving contextual integrity.

翻译：特定上下文中的数据相较于孤立解释具有更深层的意义。在分布式系统中，相互依赖的数据源能够揭示隐藏的关联与潜在结构，这为众多应用提供了宝贵的信息。本文提出渗透学习（OSM-L），这是一种自监督的分布式学习范式，旨在从分布式数据中发掘更高层次的潜在知识。OSM-L的核心是渗透过程，该过程通过提取上下文信息来合成稠密且紧凑的表示，从而无需在分布式实体间交换原始数据。OSM-L迭代地对齐局部数据表示，实现信息扩散并收敛至动态平衡，以此捕捉上下文模式。在训练过程中，它还能识别相关联的数据组，起到去中心化聚类机制的作用。实验结果在结构化数据集上验证了OSM-L的收敛性与表示能力，其在保持上下文完整性的同时，实现了局部信息对齐超过0.99的准确率。