# Osmotic Learning: A Self-Supervised Paradigm for Decentralized Contextual Data Representation

arXiv: https://arxiv.org/abs/2512.23096 · HTML: https://mlsanity.com/paper/2512.23096/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 25 references that are in the map.
- First posted on arXiv 28 Dec 2025.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [A Comprehensive Survey on Automatic Knowledge Graph Construction](https://mlsanity.com/paper/2302.05019/) (2023)
- [PTaRL: Prototype-based Tabular Representation Learning via Space Calibration](https://mlsanity.com/paper/2407.05364/) (2024)
- [Heterogeneous Contrastive Learning for Foundation Models and Beyond](https://mlsanity.com/paper/2404.00225/) (2024)
- [Teacher-Student Architecture for Knowledge Distillation: A Survey](https://mlsanity.com/paper/2308.04268/) (2023)
- [Negative Sampling for Contrastive Representation Learning: A Review](https://mlsanity.com/paper/2206.00212/) (2022)
- [Does Learning from Decentralized Non-IID Unlabeled Data Benefit from Self Supervision?](https://mlsanity.com/paper/2210.10947/) (2022)
- [A Survey on Self-Supervised Representation Learning](https://mlsanity.com/paper/2308.11455/) (2023)
- [Deep Feature Embedding for Tabular Data](https://mlsanity.com/paper/2408.17162/) (2024)
- [Task Adaptive Feature Transformation for One-Shot Learning](https://mlsanity.com/paper/2304.06832/) (2023)

## Abstract (arXiv)

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.