Knowledge Distillation for Federated Learning: a Practical Guide

Knowledge Distillation for Federated Learning: a Practical Guide

9 November 2022

Alessio Mora

Irene Tenison

Paolo Bellavista

Irina Rish

Papers citing "Knowledge Distillation for Federated Learning: a Practical Guide"

8 / 8 papers shown

Title
FedQUIT: On-Device Federated Unlearning via a Quasi-Competent Virtual Teacher Alessio Mora Lorenzo Valerio Paolo Bellavista A. Passarella FedML MU 42 2 0 14 Aug 2024
Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights Maryam Ben Driss Essaid Sabir H. Elbiaze Walid Saad 28 7 0 07 Dec 2023
A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency Jiawei Shao Zijian Li Wenqiang Sun Tailin Zhou Yuchang Sun Lumin Liu Zehong Lin Yuyi Mao Jun Zhang FedML 26 22 0 20 Jul 2023
Knowledge Distillation in Federated Edge Learning: A Survey Zhiyuan Wu Sheng Sun Yuwei Wang Min Liu Xue Jiang Runhan Li Bo Gao FedML 27 4 0 14 Jan 2023
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 173 411 0 14 Jul 2021
Federated Learning on Non-IID Data Silos: An Experimental Study Q. Li Yiqun Diao Quan Chen Bingsheng He FedML OOD 87 943 0 03 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 157 758 0 28 Sep 2019
Large scale distributed neural network training through online distillation Rohan Anil Gabriel Pereyra Alexandre Passos Róbert Ormándi George E. Dahl Geoffrey E. Hinton FedML 267 404 0 09 Apr 2018