Distributed Learning with Selective State Space Models: Architecture-Aware Convergence Analysis

Excerpt

arXiv:2610.02659v2 Announce Type: replace Abstract: Modern state space models (SSMs), such as Mamba2, provide a compelling alternative to transformers by combining linear-time sequence modeling with recurrent state-space dynamics. However, the behavior of SSMs in distributed learning settings remains poorly understood. In particular, the existing standard federated learning methods are largely architecture-agnostic, and do not account for the stability, selectivity, and state-space parameterizat