Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning

19 August 2022

Papers citing "Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning"

6 / 6 papers shown

Title
Can Public Large Language Models Help Private Cross-device Federated Learning? Boxin Wang Yibo Zhang Yuan Cao Bo-wen Li H. B. McMahan Sewoong Oh Zheng Xu Manzil Zaheer FedML 21 37 0 20 May 2023
Federated Automatic Differentiation Keith Rush Zachary B. Charles Zachary Garrett FedML 34 1 0 18 Jan 2023
Papaya: Practical, Private, and Scalable Federated Learning Dzmitry Huba John Nguyen Kshitiz Malik Ruiyu Zhu Michael G. Rabbat ... H. Srinivas Kaikai Wang Anthony Shoumikhin Jesik Min Mani Malek FedML 105 137 0 08 Nov 2021
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
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 226 4,460 0 23 Jan 2020
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization Amirhossein Reisizadeh Aryan Mokhtari Hamed Hassani Ali Jadbabaie Ramtin Pedarsani FedML 159 760 0 28 Sep 2019