Understanding Top-k Sparsification in Distributed Deep Learning

Understanding Top-k Sparsification in Distributed Deep Learning

20 November 2019

Papers citing "Understanding Top-k Sparsification in Distributed Deep Learning"

10 / 10 papers shown

Title
Sparsification Under Siege: Defending Against Poisoning Attacks in Communication-Efficient Federated Learning Zhiyong Jin Runhua Xu C. Li Y. Liu Jianxin Li AAML FedML 37 0 0 30 Apr 2025
Delayed Random Partial Gradient Averaging for Federated Learning Xinyi Hu FedML 39 0 0 31 Dec 2024
Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization Zhe Li Bicheng Ying Zidong Liu Haibo Yang Haibo Yang FedML 59 3 0 24 May 2024
GraVAC: Adaptive Compression for Communication-Efficient Distributed DL Training S. Tyagi Martin Swany 17 4 0 20 May 2023
Personalized Privacy-Preserving Framework for Cross-Silo Federated Learning Van Tuan Tran Huy Hieu Pham Kok-Seng Wong FedML 21 7 0 22 Feb 2023
Towards Efficient Communications in Federated Learning: A Contemporary Survey Zihao Zhao Yuzhu Mao Yang Liu Linqi Song Ouyang Ye Xinlei Chen Wenbo Ding FedML 43 59 0 02 Aug 2022
DNN gradient lossless compression: Can GenNorm be the answer? Zhongzhu Chen Eduin E. Hernandez Yu-Chih Huang Stefano Rini 9 9 0 15 Nov 2021
Rethinking gradient sparsification as total error minimization Atal Narayan Sahu Aritra Dutta A. Abdelmoniem Trambak Banerjee Marco Canini Panos Kalnis 35 54 0 02 Aug 2021
GOBO: Quantizing Attention-Based NLP Models for Low Latency and Energy Efficient Inference Ali Hadi Zadeh Isak Edo Omar Mohamed Awad Andreas Moshovos MQ 9 183 0 08 May 2020
Communication-Efficient Decentralized Learning with Sparsification and Adaptive Peer Selection Zhenheng Tang S. Shi X. Chu FedML 13 57 0 22 Feb 2020