Osmotic Learning: A Self-Supervised Paradigm for Decentralized Contextual Data Representation
This addresses the challenge of extracting contextual patterns from interdependent data sources in distributed systems, which is incremental as it builds on existing self-supervised and distributed learning methods.
The paper tackles the problem of uncovering higher-level latent knowledge from distributed data by introducing Osmotic Learning (OSM-L), a self-supervised distributed learning paradigm that synthesizes dense representations without raw data exchange, achieving over 0.99 accuracy in local information alignment.
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.