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

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

11 February 2022

Nicolò Dal Fabbro

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

9 / 9 papers shown

Title
Accelerated Training of Federated Learning via Second-Order Methods Mrinmay Sen Sidhant R Nair C Krishna Mohan FedML 52 0 0 29 May 2025
FLeNS: Federated Learning with Enhanced Nesterov-Newton Sketch Sunny Gupta Mohit Jindal Pankhi Kashyap Pranav Jeevan Amit Sethi FedML 75 0 0 23 Sep 2024
Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacy Wei Huo Changxin Liu Kemi Ding Karl H. Johansson Ling Shi FedML 99 0 0 08 Aug 2024
FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning Jian Li Yong Liu Wei Wang Haoran Wu Weiping Wang FedML 83 3 0 05 Jan 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 100 1 0 30 Nov 2023
Distributed Adaptive Greedy Quasi-Newton Methods with Explicit Non-asymptotic Convergence Bounds Yubo Du Keyou You 88 5 0 30 Nov 2023
FedZeN: Towards superlinear zeroth-order federated learning via incremental Hessian estimation A. Maritan S. Dey Luca Schenato FedML 90 6 0 29 Sep 2023
Q-SHED: Distributed Optimization at the Edge via Hessian Eigenvectors Quantization Nicolò Dal Fabbro M. Rossi Luca Schenato S. Dey 53 0 0 18 May 2023
Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation Rustem Islamov Xun Qian Slavomír Hanzely M. Safaryan Peter Richtárik 79 16 0 07 Jun 2022