Federated Automatic Differentiation

18 January 2023

Papers citing "Federated Automatic Differentiation"

8 / 8 papers shown

Title
Simple and Effective Gradient-Based Tuning of Sequence-to-Sequence Models Jared Lichtarge Chris Alberti Shankar Kumar 26 4 0 10 Sep 2022
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 97 133 0 08 Nov 2021
Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications Matthias Paulik M. Seigel Henry Mason Dominic Telaar Joris Kluivers ... Dominic Hughes O. Javidbakht Fei Dong Rehan Rishi Stanley Hung FedML 167 124 0 16 Feb 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
Federated Bayesian Optimization via Thompson Sampling Zhongxiang Dai K. H. Low Patrick Jaillet FedML 79 109 0 20 Oct 2020
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 243 11,568 0 09 Mar 2017
Neural Architecture Search with Reinforcement Learning Barret Zoph Quoc V. Le 264 5,290 0 05 Nov 2016
Variational Optimization J. Staines David Barber DRL 55 52 0 18 Dec 2012