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

TL;DR

Osmotic Learning (OSM-L) is a self-supervised, decentralized method that uncovers latent contextual knowledge from distributed data without raw data exchange, achieving high accuracy in data alignment and clustering.

Contribution

The paper introduces OSM-L, a novel self-supervised paradigm that enables decentralized extraction of contextual representations and clustering without raw data sharing.

Findings

Achieves over 0.99 accuracy in local data alignment.

Effectively uncovers latent contextual patterns.

Converges reliably on structured datasets.

Abstract

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.