Byzantine-Robust Muon: Resilient Momentum Orthogonalization for Distributed Learning

Summary

This paper analyzes Byzantine-robust distributed and federated learning with the Muon optimizer, using a unified first-order trust-region framework and robust aggregation, without assuming i.i.d. data. It establishes convergence guarantees for non-convex and star-convex objectives, with an additive heterogeneity error floor. In simulated federated experiments on CIFAR-10, TinyStories and other benchmarks, the tested Muon configurations achieved higher attacked accuracy or lower perplexity than the tested baselines.

Related items

• HighCVE-2026-63651: LiveDocs is a primary-source-first documentation for AI agents. Prior to version 0.7.0, fetch_docs and introspect…Similar attack · NVD/CVE Database
• HighCVE-2026-125271 - AWS API MCP Server Security Policy Bypass via Incorrect Operation Name NormalizationSimilar attack · AWS Security Bulletins
• InfoLocation Matters: Sparse Latent-Variance Attacks for Face Privacy against Diffusion PersonalizationSimilar attack · Arxiv (cs.RO + cs.CV security)
• MediumCVE-2026-83953 GitHub Copilot for Jetbrains Information Disclosure VulnerabilitySimilar attack · MSRC Security Update Guide
• HighGHSA-ghwv-vrp2-2975: Cocos AI Intra-handshake attested TLS implementation is vulnerable to TOCTOU AttacksSimilar attack · GitHub Advisory Database