FAR-SIGN achieves adversary-resilient fully asynchronous optimization via signed directional projections and two-timescale correction, with almost-sure convergence to stationary points at rates O(n^{-1/4+ε}) first-order and O(n^{-1/6+ε}) zeroth-order.
On the byzantine fault tolerance of signSGD with majority vote.CoRR, abs/2502.19170
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SignMuon merges majority-vote sign aggregation from signSGD with Muon's polar-factor steps to create a communication-efficient distributed optimizer that matches signSGD rates under symmetric noise and shows strong empirical results on CIFAR and nanoGPT.
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Adversary-Robust Learning from Fully Asynchronous Directional Derivative Estimates
FAR-SIGN achieves adversary-resilient fully asynchronous optimization via signed directional projections and two-timescale correction, with almost-sure convergence to stationary points at rates O(n^{-1/4+ε}) first-order and O(n^{-1/6+ε}) zeroth-order.
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SignMuon: Communication-Efficient Distributed Muon Optimization
SignMuon merges majority-vote sign aggregation from signSGD with Muon's polar-factor steps to create a communication-efficient distributed optimizer that matches signSGD rates under symmetric noise and shows strong empirical results on CIFAR and nanoGPT.