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Federated Learning with Autotuned Communication-Efficient Secure
  Aggregation

Federated Learning with Autotuned Communication-Efficient Secure Aggregation

30 November 2019
Keith Bonawitz
Fariborz Salehi
Jakub Konecný
H. B. McMahan
Marco Gruteser
    FedML
ArXivPDFHTML

Papers citing "Federated Learning with Autotuned Communication-Efficient Secure Aggregation"

15 / 15 papers shown
Title
Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy
Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy
Jiahao Xu
Rui Hu
Olivera Kotevska
FedML
6
0
0
19 May 2025
A Survey of What to Share in Federated Learning: Perspectives on Model
  Utility, Privacy Leakage, and Communication Efficiency
A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency
Jiawei Shao
Zijian Li
Wenqiang Sun
Tailin Zhou
Yuchang Sun
Lumin Liu
Zehong Lin
Yuyi Mao
Jun Zhang
FedML
43
23
0
20 Jul 2023
The Future of Consumer Edge-AI Computing
The Future of Consumer Edge-AI Computing
Stefanos Laskaridis
Stylianos I. Venieris
Alexandros Kouris
Rui Li
Nicholas D. Lane
47
8
0
19 Oct 2022
Dap-FL: Federated Learning flourishes by adaptive tuning and secure
  aggregation
Dap-FL: Federated Learning flourishes by adaptive tuning and secure aggregation
Qian Chen
Zilong Wang
Jiawei Chen
Haonan Yan
Xiaodong Lin
FedML
10
17
0
08 Jun 2022
Survey on Federated Learning Threats: concepts, taxonomy on attacks and
  defences, experimental study and challenges
Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges
Nuria Rodríguez-Barroso
Daniel Jiménez López
M. V. Luzón
Francisco Herrera
Eugenio Martínez-Cámara
FedML
37
212
0
20 Jan 2022
Resource-Efficient Federated Learning
Resource-Efficient Federated Learning
A. Abdelmoniem
Atal Narayan Sahu
Marco Canini
Suhaib A. Fahmy
FedML
32
55
0
01 Nov 2021
Private Multi-Task Learning: Formulation and Applications to Federated
  Learning
Private Multi-Task Learning: Formulation and Applications to Federated Learning
Shengyuan Hu
Zhiwei Steven Wu
Virginia Smith
FedML
27
19
0
30 Aug 2021
The Distributed Discrete Gaussian Mechanism for Federated Learning with
  Secure Aggregation
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation
Peter Kairouz
Ziyu Liu
Thomas Steinke
FedML
44
232
0
12 Feb 2021
Information Theoretic Secure Aggregation with User Dropouts
Information Theoretic Secure Aggregation with User Dropouts
Yizhou Zhao
Hua Sun
FedML
59
67
0
19 Jan 2021
Design and Analysis of Uplink and Downlink Communications for Federated
  Learning
Design and Analysis of Uplink and Downlink Communications for Federated Learning
Sihui Zheng
Cong Shen
Xiang Chen
39
140
0
07 Dec 2020
A Systematic Literature Review on Federated Machine Learning: From A
  Software Engineering Perspective
A Systematic Literature Review on Federated Machine Learning: From A Software Engineering Perspective
Sin Kit Lo
Qinghua Lu
Chen Wang
Hye-Young Paik
Liming Zhu
FedML
48
83
0
22 Jul 2020
Byzantine-Resilient Secure Federated Learning
Byzantine-Resilient Secure Federated Learning
Jinhyun So
Başak Güler
A. Avestimehr
FedML
19
238
0
21 Jul 2020
Characterizing Impacts of Heterogeneity in Federated Learning upon
  Large-Scale Smartphone Data
Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data
Chengxu Yang
Qipeng Wang
Mengwei Xu
Shangguang Wang
Kaigui Bian
Yunxin Liu
Xuanzhe Liu
24
22
0
12 Jun 2020
Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure
  Federated Learning
Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning
Jinhyun So
Başak Güler
A. Avestimehr
FedML
27
289
0
11 Feb 2020
Differentially Private Learning with Adaptive Clipping
Differentially Private Learning with Adaptive Clipping
Galen Andrew
Om Thakkar
H. B. McMahan
Swaroop Ramaswamy
FedML
30
330
0
09 May 2019
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