English

Improved Bounds for Coin Flipping, Leader Election, and Random Selection

Computational Complexity 2026-04-30 v3 Cryptography and Security Distributed, Parallel, and Cluster Computing

Abstract

Random selection, leader election, and collective coin flipping are fundamental tasks in fault-tolerant distributed computing. We study these problems in the full-information model where despite decades of study, key gaps remain in our understanding of the trade-offs between round complexity, communication per player in each round, and adversarial resilience. We make progress by proving improved bounds for these problems. We first show that any kk-round coin flipping protocol over \ell players, each player sending one bit per round, can be biased by O(/log(k)())O(\ell/\log^{(k)}(\ell)) bad players. We obtain a similar lower bound for leader election. This strengthens prior best bounds [RSZ, SICOMP 2002] of O(/log(2k1)())O(\ell/\log^{(2k-1)}(\ell)) for coin flipping protocols and O(/log(2k+1)())O(\ell/\log^{(2k+1)}(\ell)) for leader election protocols. Our result implies that any (1-bit per player) protocol tolerating linear fraction of bad players requires at least log\log^* \ell rounds, showing existing protocols [RZ, JCSS 2001; F, FOCS 1999] are near-optimal. We next initiate the study of one-round, (1-bit per player) random selection. For all m(log())2m\ge (\log(\ell))^2, we obtain an optimal protocol (a first in the full information model for any task): We construct a protocol resilient to O(/m)O(\ell / m) bad players that outputs mm uniform random bits. And, we show that any protocol that outputs mm uniform random bits can be corrupted using O(/m)O(\ell / m) bad players. This also implies a one-round leader election protocol resilient to /(log)2\ell / (\log \ell)^2 bad players, improving the prior best protocol [RZ, JCSS 2001] which was resilient to /(log)3\ell / (\log \ell)^3 bad players. Our resilience matches that of the best one-round coin flipping protocol by Ajtai & Linial. To obtain our lower bound, we introduce multi-output influence, an extension of influence of boolean functions to the multi-output setting.

Keywords

Cite

@article{arxiv.2504.01856,
  title  = {Improved Bounds for Coin Flipping, Leader Election, and Random Selection},
  author = {Eshan Chattopadhyay and Mohit Gurumukhani and Noam Ringach and Rocco A. Servedio},
  journal= {arXiv preprint arXiv:2504.01856},
  year   = {2026}
}
R2 v1 2026-06-28T22:44:06.327Z