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• W2947683833 abstract "How can we efficiently mitigate the overhead of gradient communications in distributed optimization? This problem is at the heart of training scalable machine learning models and has been mainly studied in the unconstrained setting. In this paper, we propose Quantized-Frank-Wolfe (QFW), the first projection-free and communication-efficient algorithm for solving constrained optimization problems at scale. We consider both convex and non-convex objective functions, expressed as a finite-sum or more generally a stochastic optimization problem, and provide strong theoretical guarantees on the convergence rate of QFW. This is accomplished by proposing novel quantization schemes that efficiently compress gradients while controlling the noise variance introduced during this process. Finally, we empirically validate the efficiency of QFW in terms of communication and the quality of returned solution against natural baselines." @default.
• W2947683833 created "2019-06-07" @default.
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• W2947683833 date "2019-02-17" @default.
• W2947683833 modified "2023-09-23" @default.
• W2947683833 title "Quantized Frank-Wolfe: Faster Optimization, Lower Communication, and Projection Free" @default.
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