Dap-FL: Federated Learning flourishes by adaptive tuning and secure aggregation

8 June 2022

Papers citing "Dap-FL: Federated Learning flourishes by adaptive tuning and secure aggregation"

21 / 21 papers shown

Title
Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT Peiying Zhang Chao Wang Chunxiao Jiang Zhu Han FedML 68 149 0 03 Feb 2022
PPT: A Privacy-Preserving Global Model Training Protocol for Federated Learning in P2P Networks Qian Chen Zilong Wang Wenjing Zhang Xiaodong Lin FedML 61 17 0 30 May 2021
Cost-Effective Federated Learning Design Bing Luo Xiang Li Shiqiang Wang Jianwei Huang Leandros Tassiulas FedML 64 182 0 15 Dec 2020
Adaptive Federated Learning and Digital Twin for Industrial Internet of Things Wen Sun S. Lei Lu Wang Zhiqiang Liu Yan Zhang FedML AI4CE 125 203 0 25 Oct 2020
TiFL: A Tier-based Federated Learning System Zheng Chai Ahsan Ali Syed Zawad Stacey Truex Ali Anwar Nathalie Baracaldo Yi Zhou Heiko Ludwig Feng Yan Yue Cheng FedML 49 281 0 25 Jan 2020
Federated Learning with Autotuned Communication-Efficient Secure Aggregation Keith Bonawitz Fariborz Salehi Jakub Konecný H. B. McMahan Marco Gruteser FedML 83 74 0 30 Nov 2019
Device Scheduling with Fast Convergence for Wireless Federated Learning Misha Sra C. Schmandt Z. Niu FedML 74 197 0 03 Nov 2019
Resource Allocation in Mobility-Aware Federated Learning Networks: A Deep Reinforcement Learning Approach H. T. Nguyen Nguyen Cong Luong Jun Zhao Chau Yuen Dusit Niyato 59 56 0 21 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 87 463 0 26 Sep 2019
A Joint Learning and Communications Framework for Federated Learning over Wireless Networks Mingzhe Chen Zhaohui Yang Walid Saad Changchuan Yin H. Vincent Poor Shuguang Cui FedML 78 1,191 0 17 Sep 2019
On the Convergence of FedAvg on Non-IID Data Xiang Li Kaixuan Huang Wenhao Yang Shusen Wang Zhihua Zhang FedML 149 2,348 0 04 Jul 2019
Differentially Private Learning with Adaptive Clipping Galen Andrew Om Thakkar H. B. McMahan Swaroop Ramaswamy FedML 84 341 0 09 May 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
Actor-Attention-Critic for Multi-Agent Reinforcement Learning Shariq Iqbal Fei Sha 69 752 0 05 Oct 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
A Brief Survey of Deep Reinforcement Learning Kai Arulkumaran M. Deisenroth Miles Brundage Anil Anthony Bharath OffRL 127 2,824 0 19 Aug 2017
Membership Inference Attacks against Machine Learning Models Reza Shokri M. Stronati Congzheng Song Vitaly Shmatikov SLR MIALM MIACV 272 4,159 0 18 Oct 2016
Federated Learning: Strategies for Improving Communication Efficiency Jakub Konecný H. B. McMahan Felix X. Yu Peter Richtárik A. Suresh Dave Bacon FedML 309 4,655 0 18 Oct 2016
Communication-Efficient Learning of Deep Networks from Decentralized Data H. B. McMahan Eider Moore Daniel Ramage S. Hampson Blaise Agüera y Arcas FedML 406 17,559 0 17 Feb 2016
Continuous control with deep reinforcement learning Timothy Lillicrap Jonathan J. Hunt Alexander Pritzel N. Heess Tom Erez Yuval Tassa David Silver Daan Wierstra 325 13,286 0 09 Sep 2015
Scalable Bayesian Optimization Using Deep Neural Networks Jasper Snoek Oren Rippel Kevin Swersky Ryan Kiros N. Satish N. Sundaram Md. Mostofa Ali Patwary P. Prabhat Ryan P. Adams BDL UQCV 95 1,045 0 19 Feb 2015