Federated Learning with Compression: Unified Analysis and Sharp Guarantees

2 July 2020

Farzin Haddadpour

Mohammad Mahdi Kamani

Papers citing "Federated Learning with Compression: Unified Analysis and Sharp Guarantees"

50 / 152 papers shown

Title
Improving Accelerated Federated Learning with Compression and Importance Sampling Michal Grudzieñ Grigory Malinovsky Peter Richtárik FedML 35 9 0 05 Jun 2023
Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape Yan Sun Li Shen Shi-Yong Chen Liang Ding Dacheng Tao FedML 34 33 0 19 May 2023
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
Lower Bounds and Accelerated Algorithms in Distributed Stochastic Optimization with Communication Compression Yutong He Xinmeng Huang Yiming Chen W. Yin Kun Yuan 31 7 0 12 May 2023
Performative Federated Learning: A Solution to Model-Dependent and Heterogeneous Distribution Shifts Kun Jin Tongxin Yin Zhong Chen Zeyu Sun Xueru Zhang Yang Liu Mingyan D. Liu OOD FedML 22 6 0 08 May 2023
Efficient Personalized Federated Learning via Sparse Model-Adaptation Daoyuan Chen Fandong Meng Dawei Gao Bolin Ding Yaliang Li FedML 113 47 0 04 May 2023
FS-Real: Towards Real-World Cross-Device Federated Learning Daoyuan Chen Dawei Gao Yuexiang Xie Xuchen Pan Zitao Li Yaliang Li Bolin Ding Jingren Zhou 117 26 0 23 Mar 2023
FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy Yan Sun Li Shen Tiansheng Huang Liang Ding Dacheng Tao FedML 36 51 0 21 Feb 2023
Federated Gradient Matching Pursuit Halyun Jeong Deanna Needell Jing Qin FedML 37 1 0 20 Feb 2023
TAMUNA: Doubly Accelerated Distributed Optimization with Local Training, Compression, and Partial Participation Laurent Condat Ivan Agarský Grigory Malinovsky Peter Richtárik FedML 34 4 0 20 Feb 2023
Magnitude Matters: Fixing SIGNSGD Through Magnitude-Aware Sparsification in the Presence of Data Heterogeneity Richeng Jin Xiaofan He C. Zhong Zhaoyang Zhang Tony Q.S. Quek H. Dai FedML 21 1 0 19 Feb 2023
$z$ -SignFedAvg: A Unified Stochastic Sign-based Compression for Federated Learning Zhiwei Tang Yanmeng Wang Tsung-Hui Chang FedML 21 14 0 06 Feb 2023
DoCoFL: Downlink Compression for Cross-Device Federated Learning Ron Dorfman S. Vargaftik Y. Ben-Itzhak Kfir Y. Levy FedML 32 18 0 01 Feb 2023
A Communication-Efficient Adaptive Algorithm for Federated Learning under Cumulative Regret Sudeep Salgia Qing Zhao T. Gabay Kobi Cohen FedML 29 10 0 21 Jan 2023
Federated Automatic Differentiation Keith Rush Zachary B. Charles Zachary Garrett FedML 34 1 0 18 Jan 2023
Network Adaptive Federated Learning: Congestion and Lossy Compression Parikshit Hegde G. Veciana Aryan Mokhtari FedML 20 4 0 11 Jan 2023
Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning A. Mitra George J. Pappas Hamed Hassani 21 12 0 03 Jan 2023
Federated Learning with Flexible Control Shiqiang Wang Jake B. Perazzone Mingyue Ji Kevin S. Chan FedML 28 17 0 16 Dec 2022
Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression Xiaoyun Li Ping Li FedML 34 4 0 25 Nov 2022
Optimal Privacy Preserving for Federated Learning in Mobile Edge Computing Hai M. Nguyen N. Chu Diep N. Nguyen D. Hoang Van-Dinh Nguyen Minh Hoàng Hà E. Dutkiewicz Marwan Krunz FedML 27 1 0 14 Nov 2022
FedRule: Federated Rule Recommendation System with Graph Neural Networks Yuhang Yao Mohammad Mahdi Kamani Zhongwei Cheng Lin Chen Carlee Joe-Wong Tianqiang Liu 17 17 0 13 Nov 2022
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design Chuan Guo Kamalika Chaudhuri Pierre Stock Michael G. Rabbat FedML 33 7 0 08 Nov 2022
Distributed Linear Bandits under Communication Constraints Sudeep Salgia Qing Zhao 32 7 0 04 Nov 2022
A Convergence Theory for Federated Average: Beyond Smoothness Xiaoxiao Li Zhao Song Runzhou Tao Guangyi Zhang FedML 32 5 0 03 Nov 2022
Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms Vincent Plassier Alain Durmus Eric Moulines FedML 21 6 0 31 Oct 2022
ModularFed: Leveraging Modularity in Federated Learning Frameworks Mohamad Arafeh Hadi Otrok Hakima Ould-Slimane Azzam Mourad C. Talhi Ernesto Damiani 30 19 0 31 Oct 2022
Provably Doubly Accelerated Federated Learning: The First Theoretically Successful Combination of Local Training and Communication Compression Laurent Condat Ivan Agarský Peter Richtárik FedML 34 17 0 24 Oct 2022
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings Jean Ogier du Terrail Samy Ayed Edwige Cyffers Felix Grimberg Chaoyang He ... Sai Praneeth Karimireddy Marco Lorenzi Giovanni Neglia Marc Tommasi M. Andreux FedML 47 142 0 10 Oct 2022
Sparse Random Networks for Communication-Efficient Federated Learning Berivan Isik Francesco Pase Deniz Gunduz Tsachy Weissman M. Zorzi FedML 70 52 0 30 Sep 2022
Personalized Federated Learning with Communication Compression El Houcine Bergou Konstantin Burlachenko Aritra Dutta Peter Richtárik FedML 77 9 0 12 Sep 2022
On the Convergence of Multi-Server Federated Learning with Overlapping Area Zhe Qu Xingyu Li Jie Xu Bo Tang Zhuo Lu Yao-Hong Liu FedML 50 14 0 16 Aug 2022
Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computation Liang Li Chenpei Huang Dian Shi Hao Wang Xiangwei Zhou Minglei Shu Miao Pan FedML 39 9 0 15 Aug 2022
Towards Efficient Communications in Federated Learning: A Contemporary Survey Zihao Zhao Yuzhu Mao Yang Liu Linqi Song Ouyang Ye Xinlei Chen Wenbo Ding FedML 54 59 0 02 Aug 2022
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels Yaodong Yu Alexander Wei Sai Praneeth Karimireddy Yi Ma Michael I. Jordan FedML 17 30 0 13 Jul 2022
Communication-Efficient Federated Learning With Data and Client Heterogeneity Hossein Zakerinia Shayan Talaei Giorgi Nadiradze Dan Alistarh FedML 19 7 0 20 Jun 2022
Federated Optimization Algorithms with Random Reshuffling and Gradient Compression Abdurakhmon Sadiev Grigory Malinovsky Eduard A. Gorbunov Igor Sokolov Ahmed Khaled Konstantin Burlachenko Peter Richtárik FedML 16 21 0 14 Jun 2022
Federated Adversarial Training with Transformers Ahmed Aldahdooh W. Hamidouche Olivier Déforges FedML ViT 25 2 0 05 Jun 2022
Straggler-Resilient Personalized Federated Learning Isidoros Tziotis Zebang Shen Ramtin Pedarsani Hamed Hassani Aryan Mokhtari FedML 33 9 0 05 Jun 2022
Federated Learning with a Sampling Algorithm under Isoperimetry Lukang Sun Adil Salim Peter Richtárik FedML 26 7 0 02 Jun 2022
Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top Eduard A. Gorbunov Samuel Horváth Peter Richtárik Gauthier Gidel AAML 19 0 0 01 Jun 2022
Edge Learning for B5G Networks with Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing Wei Xu Zhaohui Yang Derrick Wing Kwan Ng Marco Levorato Yonina C. Eldar Mérouane Debbah 28 398 0 01 Jun 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
Test-Time Robust Personalization for Federated Learning Liang Jiang Tao R. Lin FedML OOD TTA 82 43 0 22 May 2022
Federated Random Reshuffling with Compression and Variance Reduction Grigory Malinovsky Peter Richtárik FedML 27 10 0 08 May 2022
Communication-Efficient Adaptive Federated Learning Yujia Wang Lu Lin Jinghui Chen FedML 27 71 0 05 May 2022
On the Convergence of Momentum-Based Algorithms for Federated Bilevel Optimization Problems Hongchang Gao FedML 28 1 0 28 Apr 2022
A review of Federated Learning in Intrusion Detection Systems for IoT Aitor Belenguer J. Navaridas J. A. Pascual 25 15 0 26 Apr 2022
Federated Progressive Sparsification (Purge, Merge, Tune)+ Dimitris Stripelis Umang Gupta Greg Ver Steeg J. Ambite FedML 23 9 0 26 Apr 2022
FedVQCS: Federated Learning via Vector Quantized Compressed Sensing Yong-Nam Oh Yo-Seb Jeon Mingzhe Chen Walid Saad FedML 30 10 0 16 Apr 2022
FedSynth: Gradient Compression via Synthetic Data in Federated Learning Shengyuan Hu Jack Goetz Kshitiz Malik Hongyuan Zhan Zhe Liu Yue Liu DD FedML 37 38 0 04 Apr 2022