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- W4285280776 abstract "We study the multi-step Model-Agnostic Meta-Learning (MAML) framework where a group of <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> <tex-math notation=LaTeX>$n$ </tex-math></inline-formula> agents seeks to find a common point that enables “few-shot” learning (personalization) via local stochastic gradient steps on their local functions. We formulate the personalized optimization problem under the MAML framework and propose PARS-Push, a decentralized asynchronous algorithm robust to message failures, communication delays, and directed message sharing. We characterize the convergence rate of PARS-Push under arbitrary multi-step personalization for smooth strongly convex, and smooth non-convex functions. Moreover, we provide numerical experiments showing its performance under heterogeneous data setups." @default.
- W4285280776 created "2022-07-14" @default.
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- W4285280776 date "2023-01-01" @default.
- W4285280776 modified "2023-09-24" @default.
- W4285280776 title "PARS-Push: Personalized, Asynchronous and Robust Decentralized Optimization" @default.
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- W4285280776 doi "https://doi.org/10.1109/lcsys.2022.3189317" @default.
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