Federated Dynamical Low-Rank Training with Global Loss Convergence Guarantees

25 June 2024

Papers citing "Federated Dynamical Low-Rank Training with Global Loss Convergence Guarantees"

5 / 5 papers shown

Title
Low-rank lottery tickets: finding efficient low-rank neural networks via matrix differential equations Steffen Schotthöfer Emanuele Zangrando J. Kusch Gianluca Ceruti Francesco Tudisco 50 30 0 26 May 2022
Initialization and Regularization of Factorized Neural Layers M. Khodak Neil A. Tenenholtz Lester W. Mackey Nicolò Fusi 63 56 0 03 May 2021
Communication-Efficient Federated Learning with Dual-Side Low-Rank Compression Zhefeng Qiao Xianghao Yu Jun Zhang Khaled B. Letaief FedML 28 19 0 26 Apr 2021
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients A. Mitra Rayana H. Jaafar George J. Pappas Hamed Hassani FedML 55 157 0 14 Feb 2021
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization Amirhossein Reisizadeh Aryan Mokhtari Hamed Hassani Ali Jadbabaie Ramtin Pedarsani FedML 157 756 0 28 Sep 2019