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Peter Richtárik

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FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity

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Apr 15, 2024
Kai Yi, Nidham Gazagnadou, Peter Richtárik, Lingjuan Lyu

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FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models

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Mar 14, 2024
Kai Yi, Georg Meinhardt, Laurent Condat, Peter Richtárik

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Streamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reduction

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Mar 11, 2024
Yury Demidovich, Grigory Malinovsky, Peter Richtárik

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LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression

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Mar 07, 2024
Laurent Condat, Artavazd Maranjyan, Peter Richtárik

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Error Feedback Reloaded: From Quadratic to Arithmetic Mean of Smoothness Constants

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Feb 16, 2024
Peter Richtárik, Elnur Gasanov, Konstantin Burlachenko

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Improving the Worst-Case Bidirectional Communication Complexity for Nonconvex Distributed Optimization under Function Similarity

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Feb 09, 2024
Kaja Gruntkowska, Alexander Tyurin, Peter Richtárik

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Shadowheart SGD: Distributed Asynchronous SGD with Optimal Time Complexity Under Arbitrary Computation and Communication Heterogeneity

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Feb 07, 2024
Alexander Tyurin, Marta Pozzi, Ivan Ilin, Peter Richtárik

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Correlated Quantization for Faster Nonconvex Distributed Optimization

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Jan 10, 2024
Andrei Panferov, Yury Demidovich, Ahmad Rammal, Peter Richtárik

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Kimad: Adaptive Gradient Compression with Bandwidth Awareness

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Dec 13, 2023
Jihao Xin, Ivan Ilin, Shunkang Zhang, Marco Canini, Peter Richtárik

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MAST: Model-Agnostic Sparsified Training

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Nov 27, 2023
Yury Demidovich, Grigory Malinovsky, Egor Shulgin, Peter Richtárik

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