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MURANA: A Generic Framework for Stochastic Variance-Reduced Optimization
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We propose a generic variance-reduced algorithm, which we call MUltiple RANdomized Algorithm (MURANA), for minimizing a sum of several smooth functions plus a regularizer, in a sequential or distributed manner. Our method is formulated with general stochastic operators, which allow us to model various strategies for reducing the computational complexity. For example, MURANA supports sparse activation of the gradients, and also reduction of the communication load via compression of the update vectors. This versatility allows MURANA to cover many existing randomization mechanisms within a unified framework, which also makes it possible to design new methods as special cases.
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Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization
A thesis proving communication-acceleration guarantees for local-step, compressed, Byzantine-robust, and low-rank federated optimization methods, assembled from the author's own published papers.
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