Decentralized Coupled Representation Learning

The learning dynamics of decentralized coupled representation learning can be formulated as a non-autonomous stochastic dynamical system, yet the macroscopic behavior of the resulting system on continuous manifolds remains poorly understood. We establish three theoretical results for such coupled slow-fast dynamics. First, under a standard random geometric graph scaling regime, we prove that the infinitesimal generator of the discrete Markov process converges to the generator of an overdamped Langevin diffusion for every $f\in C^3(\mathcal{M})$. Second, under timescale separation, the stochastic parameter dynamics converge to a deterministic averaged flow that admits a row-orthogonality Lyapunov function, controlling row-space degeneracy and ruling out parameter divergence. Third, under a spectral gap condition, the row space of the averaged parameter trajectory converges to the principal eigenspace of the induced covariance matrix. Together, these results give a stochastic-approximation limit theory for the coupled regime in which Markovian sampling and local subspace learning co-evolve, showing that memoryless local interactions can induce a stable and structured macroscopic limit.