Federated Learning Can Find Friends That Are Advantageous

7 February 2024

Papers citing "Federated Learning Can Find Friends That Are Advantageous"

8 / 8 papers shown

Title
Collaborative and Efficient Personalization with Mixtures of Adaptors Abdulla Jasem Almansoori Samuel Horváth Martin Takáč FedML 44 2 0 04 Oct 2024
FedPeWS: Personalized Warmup via Subnetworks for Enhanced Heterogeneous Federated Learning Nurbek Tastan Samuel Horváth Martin Takáč Karthik Nandakumar FedML 57 0 0 03 Oct 2024
Gradient-based Bi-level Optimization for Deep Learning: A Survey Can Chen Xiangshan Chen Chen-li Ma Zixuan Liu Xue Liu 86 35 0 24 Jul 2022
A framework for bilevel optimization that enables stochastic and global variance reduction algorithms Mathieu Dagréou Pierre Ablin Samuel Vaiter Thomas Moreau 139 96 0 31 Jan 2022
A Field Guide to Federated Optimization Jianyu Wang Zachary B. Charles Zheng Xu Gauri Joshi H. B. McMahan ... Mi Zhang Tong Zhang Chunxiang Zheng Chen Zhu Wennan Zhu FedML 187 411 0 14 Jul 2021
Privacy and Robustness in Federated Learning: Attacks and Defenses Lingjuan Lyu Han Yu Xingjun Ma Chen Chen Lichao Sun Jun Zhao Qiang Yang Philip S. Yu FedML 183 355 0 07 Dec 2020
Survey of Personalization Techniques for Federated Learning V. Kulkarni Milind Kulkarni Aniruddha Pant FedML 182 326 0 19 Mar 2020
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition Hamed Karimi J. Nutini Mark W. Schmidt 139 1,199 0 16 Aug 2016