On the Outsized Importance of Learning Rates in Local Update Methods

2 July 2020

Papers citing "On the Outsized Importance of Learning Rates in Local Update Methods"

20 / 20 papers shown

Title
The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication Kumar Kshitij Patel Margalit Glasgow Ali Zindari Lingxiao Wang Sebastian U. Stich Ziheng Cheng Nirmit Joshi Nathan Srebro 48 6 0 19 May 2024
Federated TD Learning over Finite-Rate Erasure Channels: Linear Speedup under Markovian Sampling Nicolò Dal Fabbro A. Mitra George J. Pappas FedML 35 12 0 14 May 2023
Federated Temporal Difference Learning with Linear Function Approximation under Environmental Heterogeneity Han Wang A. Mitra Hamed Hassani George J. Pappas James Anderson FedML 29 21 0 04 Feb 2023
Straggler-Resilient Differentially-Private Decentralized Learning Yauhen Yakimenka Chung-Wei Weng Hsuan-Yin Lin E. Rosnes J. Kliewer 29 6 0 06 Dec 2022
Federated Hypergradient Descent A. K. Kan FedML 37 3 0 03 Nov 2022
Online Meta-Learning for Model Update Aggregation in Federated Learning for Click-Through Rate Prediction Xianghang Liu Bartlomiej Twardowski Tri Kurniawan Wijaya FedML 31 1 0 30 Aug 2022
FedAvg with Fine Tuning: Local Updates Lead to Representation Learning Liam Collins Hamed Hassani Aryan Mokhtari Sanjay Shakkottai FedML 32 75 0 27 May 2022
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback Boxin Zhao Lingxiao Wang Mladen Kolar Ziqi Liu Qing Cui Jun Zhou Chaochao Chen FedML 34 10 0 28 Dec 2021
On Convergence of Federated Averaging Langevin Dynamics Wei Deng Qian Zhang Yi Ma Zhao-quan Song Guang Lin FedML 30 16 0 09 Dec 2021
A Marketplace for Trading AI Models based on Blockchain and Incentives for IoT Data L. Nguyen Shashi Raj Pandey B. Soret A. Broering P. Popovski 42 20 0 06 Dec 2021
What Do We Mean by Generalization in Federated Learning? Honglin Yuan Warren Morningstar Lin Ning K. Singhal OOD FedML 41 71 0 27 Oct 2021
FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learning Charlie Hou K. K. Thekumparampil Giulia Fanti Sewoong Oh FedML 30 14 0 16 Aug 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 187 412 0 14 Jul 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 Learning via Posterior Averaging: A New Perspective and Practical Algorithms Maruan Al-Shedivat Jennifer Gillenwater Eric P. Xing Afshin Rostamizadeh FedML 19 109 0 11 Oct 2020
Effective Federated Adaptive Gradient Methods with Non-IID Decentralized Data Qianqian Tong Guannan Liang J. Bi FedML 41 27 0 14 Sep 2020
Adaptive Federated Optimization Sashank J. Reddi Zachary B. Charles Manzil Zaheer Zachary Garrett Keith Rush Jakub Konecný Sanjiv Kumar H. B. McMahan FedML 20 1,391 0 29 Feb 2020
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization Amirhossein Reisizadeh Aryan Mokhtari Hamed Hassani Ali Jadbabaie Ramtin Pedarsani FedML 174 760 0 28 Sep 2019
Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML Aniruddh Raghu M. Raghu Samy Bengio Oriol Vinyals 186 639 0 19 Sep 2019
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 359 11,684 0 09 Mar 2017