v1v2v3v4v5 (latest)

FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout

26 February 2021

Stylianos I. Venieris

Nicholas D. Lane

ArXiv (abs)PDF HTML

Papers citing "FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout"

50 / 63 papers shown

Title
Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity Zhuojun Tian Zhaoyang Zhang Yiwei Li Mehdi Bennis 172 0 0 24 Apr 2025
Moss: Proxy Model-based Full-Weight Aggregation in Federated Learning with Heterogeneous Models Y. Cai Ziqi Zhang Ding Li Yao Guo Xiangqun Chen 157 0 0 13 Mar 2025
FedGrAINS: Personalized SubGraph Federated Learning with Adaptive Neighbor Sampling Emir Ceyani Han Xie Baturalp Buyukates Carl Yang Salman Avestimehr FedML 269 0 0 22 Jan 2025
Efficient Federated Finetuning of Tiny Transformers with Resource-Constrained Devices Kilian Pfeiffer Mohamed Aboelenien Ahmed R. Khalili J. Henkel 61 1 0 12 Nov 2024
FedPeWS: Personalized Warmup via Subnetworks for Enhanced Heterogeneous Federated Learning Nurbek Tastan Samuel Horváth Martin Takáč Karthik Nandakumar FedML 125 0 0 03 Oct 2024
WHALE-FL: Wireless and Heterogeneity Aware Latency Efficient Federated Learning over Mobile Devices via Adaptive Subnetwork Scheduling Huai-an Su Jiaxiang Geng Liang Li Xiaoqi Qin Yanzhao Hou Xin Fu Miao Pan Miao Pan 85 1 0 01 May 2024
FedSPU: Personalized Federated Learning for Resource-constrained Devices with Stochastic Parameter Update Ziru Niu Hai Dong •. A. K. Qin 102 2 0 18 Mar 2024
LW-FedSSL: Resource-efficient Layer-wise Federated Self-supervised Learning Ye Lin Tun Chu Myaet Thwal Le Quang Huy Minh N. H. Nguyen Choong Seon Hong FedML 99 2 0 22 Jan 2024
Exploiting Features and Logits in Heterogeneous Federated Learning Yun-Hin Chan Edith C.H. Ngai FedML 77 2 0 27 Oct 2022
Hyperparameter Transfer Learning with Adaptive Complexity Samuel Horváth Aaron Klein Peter Richtárik Cédric Archambeau 55 18 0 25 Feb 2021
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization Xiaoxiao Li Meirui Jiang Xiaofei Zhang Michael Kamp Qi Dou OOD FedML 278 821 0 15 Feb 2021
Cost-Effective Federated Learning Design Bing Luo Xiang Li Shiqiang Wang Jianwei Huang Leandros Tassiulas FedML 64 182 0 15 Dec 2020
Can Federated Learning Save The Planet? Xinchi Qiu Titouan Parcollet Daniel J. Beutel Taner Topal Akhil Mathur Nicholas D. Lane 57 81 0 13 Oct 2020
HAPI: Hardware-Aware Progressive Inference Stefanos Laskaridis Stylianos I. Venieris Hyeji Kim Nicholas D. Lane 68 47 0 10 Aug 2020
Flower: A Friendly Federated Learning Research Framework Daniel J. Beutel Taner Topal Akhil Mathur Xinchi Qiu Javier Fernandez-Marques ... Lorenzo Sani Kwing Hei Li Titouan Parcollet Pedro Porto Buarque de Gusmão Nicholas D. Lane FedML 136 809 0 28 Jul 2020
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization Jianyu Wang Qinghua Liu Hao Liang Gauri Joshi H. Vincent Poor MoMe FedML 68 1,345 0 15 Jul 2020
A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning Samuel Horváth Peter Richtárik 68 61 0 19 Jun 2020
Federated Learning With Quantized Global Model Updates M. Amiri Deniz Gunduz Sanjeev R. Kulkarni H. Vincent Poor FedML 86 132 0 18 Jun 2020
Multi-Precision Policy Enforced Training (MuPPET): A precision-switching strategy for quantised fixed-point training of CNNs A. Rajagopal D. A. Vink Stylianos I. Venieris C. Bouganis MQ 66 15 0 16 Jun 2020
Federated Learning with Matched Averaging Hongyi Wang Mikhail Yurochkin Yuekai Sun Dimitris Papailiopoulos Y. Khazaeni FedML 121 1,127 0 15 Feb 2020
Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach Pengchao Han Shiqiang Wang K. Leung FedML 74 181 0 14 Jan 2020
Think Locally, Act Globally: Federated Learning with Local and Global Representations Paul Pu Liang Terrance Liu Liu Ziyin Nicholas B. Allen Randy P. Auerbach David Brent Ruslan Salakhutdinov Louis-Philippe Morency FedML 117 563 0 06 Jan 2020
Advances and Open Problems in Federated Learning Peter Kairouz H. B. McMahan Brendan Avent A. Bellet M. Bennis ... Zheng Xu Qiang Yang Felix X. Yu Han Yu Sen Zhao FedML AI4CE 259 6,276 0 10 Dec 2019
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 523 42,559 0 03 Dec 2019
AI Benchmark: All About Deep Learning on Smartphones in 2019 Andrey D. Ignatov Radu Timofte Andrei Kulik Seungsoo Yang Ke Wang Felix Baum Max Wu Lirong Xu Luc Van Gool ELM 45 220 0 15 Oct 2019
Model Fusion via Optimal Transport Sidak Pal Singh Martin Jaggi MoMe FedML 112 237 0 12 Oct 2019
FedMD: Heterogenous Federated Learning via Model Distillation Daliang Li Junpu Wang FedML 98 861 0 08 Oct 2019
The Non-IID Data Quagmire of Decentralized Machine Learning Kevin Hsieh Amar Phanishayee O. Mutlu Phillip B. Gibbons 161 571 0 01 Oct 2019
Model Pruning Enables Efficient Federated Learning on Edge Devices Yuang Jiang Shiqiang Wang Victor Valls Bongjun Ko Wei-Han Lee Kin K. Leung Leandros Tassiulas 77 462 0 26 Sep 2019
Federated Learning: Challenges, Methods, and Future Directions Tian Li Anit Kumar Sahu Ameet Talwalkar Virginia Smith FedML 123 4,530 0 21 Aug 2019
ShrinkML: End-to-End ASR Model Compression Using Reinforcement Learning Łukasz Dudziak Mohamed S. Abdelfattah Ravichander Vipperla Stefanos Laskaridis Nicholas D. Lane OffRL 74 18 0 08 Jul 2019
Importance Estimation for Neural Network Pruning Pavlo Molchanov Arun Mallya Stephen Tyree I. Frosio Jan Kautz 3DPC 81 885 0 25 Jun 2019
Bayesian Nonparametric Federated Learning of Neural Networks Mikhail Yurochkin Mayank Agarwal S. Ghosh Kristjan Greenewald T. Hoang Y. Khazaeni FedML 129 730 0 28 May 2019
Natural Compression for Distributed Deep Learning Samuel Horváth Chen-Yu Ho L. Horvath Atal Narayan Sahu Marco Canini Peter Richtárik 69 152 0 27 May 2019
Fair Resource Allocation in Federated Learning Tian Li Maziar Sanjabi Ahmad Beirami Virginia Smith FedML 182 804 0 25 May 2019
EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices Mario Almeida Stefanos Laskaridis Ilias Leontiadis Stylianos I. Venieris Nicholas D. Lane 41 74 0 17 May 2019
Robust and Communication-Efficient Federated Learning from Non-IID Data Felix Sattler Simon Wiedemann K. Müller Wojciech Samek FedML 74 1,361 0 07 Mar 2019
Towards Federated Learning at Scale: System Design Keith Bonawitz Hubert Eichner W. Grieskamp Dzmitry Huba A. Ingerman ... H. B. McMahan Timon Van Overveldt David Petrou Daniel Ramage Jason Roselander FedML 124 2,670 0 04 Feb 2019
Expanding the Reach of Federated Learning by Reducing Client Resource Requirements S. Caldas Jakub Konecný H. B. McMahan Ameet Talwalkar 71 450 0 18 Dec 2018
Federated Optimization in Heterogeneous Networks Tian Li Anit Kumar Sahu Manzil Zaheer Maziar Sanjabi Ameet Talwalkar Virginia Smith FedML 180 5,208 0 14 Dec 2018
LEAF: A Benchmark for Federated Settings S. Caldas Sai Meher Karthik Duddu Peter Wu Tian Li Jakub Konecný H. B. McMahan Virginia Smith Ameet Talwalkar FedML 151 1,422 0 03 Dec 2018
HAQ: Hardware-Aware Automated Quantization with Mixed Precision Kuan-Chieh Wang Zhijian Liu Chengyue Wu Ji Lin Song Han MQ 129 884 0 21 Nov 2018
Balanced Sparsity for Efficient DNN Inference on GPU Zhuliang Yao Shijie Cao Wencong Xiao Chen Zhang Lanshun Nie 57 92 0 01 Nov 2018
NestDNN: Resource-Aware Multi-Tenant On-Device Deep Learning for Continuous Mobile Vision Biyi Fang Xiao Zeng Mi Zhang 3DH 80 268 0 23 Oct 2018
SNIP: Single-shot Network Pruning based on Connection Sensitivity Namhoon Lee Thalaiyasingam Ajanthan Philip Torr VLM 263 1,206 0 04 Oct 2018
How To Backdoor Federated Learning Eugene Bagdasaryan Andreas Veit Yiqing Hua D. Estrin Vitaly Shmatikov SILM FedML 97 1,922 0 02 Jul 2018
ATOMO: Communication-efficient Learning via Atomic Sparsification Hongyi Wang Scott Sievert Zachary B. Charles Shengchao Liu S. Wright Dimitris Papailiopoulos 70 354 0 11 Jun 2018
Exploiting Unintended Feature Leakage in Collaborative Learning Luca Melis Congzheng Song Emiliano De Cristofaro Vitaly Shmatikov FedML 155 1,478 0 10 May 2018
Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge Takayuki Nishio Ryo Yonetani FedML 120 1,404 0 23 Apr 2018
Adaptive Federated Learning in Resource Constrained Edge Computing Systems Shiqiang Wang Tiffany Tuor Theodoros Salonidis K. Leung C. Makaya T. He Kevin S. Chan 254 1,716 0 14 Apr 2018