SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing

11 February 2022

Papers citing "SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing"

6 / 6 papers shown

Title
Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacy Wei Huo Changxin Liu Kemi Ding Karl H. Johansson Ling Shi FedML 35 0 0 08 Aug 2024
VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated Learning Luca Ballotta Nicolò Dal Fabbro Giovanni Perin Luca Schenato Michele Rossi Giuseppe Piro 30 1 0 30 Nov 2023
Q-SHED: Distributed Optimization at the Edge via Hessian Eigenvectors Quantization Nicolò Dal Fabbro M. Rossi Luca Schenato S. Dey 16 0 0 18 May 2023
FedLess: Secure and Scalable Federated Learning Using Serverless Computing Andreas Grafberger Mohak Chadha Anshul Jindal Jianfeng Gu Michael Gerndt 36 49 0 05 Nov 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
The Future of Digital Health with Federated Learning Nicola Rieke Jonny Hancox Wenqi Li Fausto Milletari H. Roth ... Ronald M. Summers Andrew Trask Daguang Xu Maximilian Baust M. Jorge Cardoso OOD 174 1,698 0 18 Mar 2020