Web: http://arxiv.org/abs/2205.02215

Sept. 15, 2022, 1:11 a.m. | Davoud Ataee Tarzanagh, Mingchen Li, Christos Thrampoulidis, Samet Oymak

cs.LG updates on arXiv.org arxiv.org

Standard federated optimization methods successfully apply to stochastic
problems with single-level structure. However, many contemporary ML problems --
including adversarial robustness, hyperparameter tuning, and actor-critic --
fall under nested bilevel programming that subsumes minimax and compositional
optimization. In this work, we propose \fedblo: A federated alternating
stochastic gradient method to address general nested problems. We establish
provable convergence rates for \fedblo in the presence of heterogeneous data
and introduce variations for bilevel, minimax, and compositional optimization.
\fedblo introduces multiple innovations …

arxiv minimax optimization

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