• Computer Science > Distributed, Parallel, and Cluster Computing [Submitted on 19 Feb 2026] Title:Informative Trains: A Memory-Efficient Journey to a Self-Stabilizing Leader Election Algorithm in Anonymous Graphs View PDF HTML (experimental)Abstract:We study the self-stabilizing leader election problem in anonymous $n$-nodes networks. • Achieving self-stabilization with low space memory complexity is particularly challenging, and designing space-optimal leader election algorithms remains an open problem for general graphs. • In deterministic settings, it is known that $\Omega(\log \log n)$ bits of memory per node are necessary [Blin et al., Disc. • Sci., 2023], while in probabilistic settings the same lower bound holds for some values of $n$, but only for an unfair scheduler [Beauquier et al., PODC 1999]. • Several deterministic and probabilistic protocols have been proposed in models ranging from the state model to the population protocols. • However, to the best of our knowledge, existing solutions either require $\Omega(\log n)$ bits of memory per node for general worst case graphs, or achieve low state complexity only under restricted network topologies such as rings, trees, or bounded-degree graphs.
Article Summaries:
- Computer Science > Distributed, Parallel, and Cluster Computing [Submitted on 19 Feb 2026] Title:Informative Trains: A Memory-Efficient Journey to a Self-Stabilizing Leader Election Algorithm in Anonymous Graphs View PDF HTML (experimental)Abstract:We study the self-stabilizing leader election problem in anonymous $n$-nodes networks. Achieving self-stabilization with low space memory complexity is particularly challenging, and designing space-optimal leader election algorithms remains an open problem for general graphs. In deterministic settings, it is known that $\Omega(\log \log n)$ bits of
Sources: