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

TL;DR

OSM-L introduces a self-supervised, distributed paradigm where local embeddings from interconnected agents align toward a global context embedding without sharing raw data. The osmotic strategy enables dynamic diffusion and clustering, uncovering latent sub-contexts and ensuring local information preservation. The work formalizes the framework, presents a worker-master architecture with a central diffuser, and demonstrates convergence and high contextual accuracy (up to $0.99$) on synthetic datasets under noise and partial correlations. This approach offers privacy-preserving, scalable contextual representation learning for distributed systems and opens avenues for integrating attention and real-world deployment strategies.

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.

Figures (8)

• Figure 1: OSM-L architecture.
• Figure 2: Simple context. Training (a) and testing (b) data of two agents with correlated features.
• Figure 3: Complex context. Training (a) and testing (b) data of five agents. The data are structured to highlight the correlation between the two groups: one formed by $Agent_0$ and $Agent_1$, and the other by $Agent_2$--$Agent_4$.
• Figure 4: Embedding similarity matrix in the simple context.
• Figure 5: Embedding similarity matrices in the simple context with two additional misleading agents.