ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2205.04855
  4. Cited By
Secure Distributed/Federated Learning: Prediction-Privacy Trade-Off for
  Multi-Agent System

Secure Distributed/Federated Learning: Prediction-Privacy Trade-Off for Multi-Agent System

24 April 2022
Mohamed Ridha Znaidi
Gaurav Gupta
P. Bogdan
    FedML
ArXiv (abs)PDFHTML

Papers citing "Secure Distributed/Federated Learning: Prediction-Privacy Trade-Off for Multi-Agent System"

19 / 19 papers shown
Title
Distributed Reinforcement Learning for Decentralized Linear Quadratic
  Control: A Derivative-Free Policy Optimization Approach
Distributed Reinforcement Learning for Decentralized Linear Quadratic Control: A Derivative-Free Policy Optimization Approach
Yingying Li
Yujie Tang
Runyu Zhang
Na Li
74
102
0
19 Dec 2019
Advances and Open Problems in Federated Learning
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
FedMLAI4CE
259
6,285
0
10 Dec 2019
Differentially Private Meta-Learning
Differentially Private Meta-Learning
Jeffrey Li
M. Khodak
S. Caldas
Ameet Talwalkar
FedML
124
108
0
12 Sep 2019
Federated Learning: Challenges, Methods, and Future Directions
Federated Learning: Challenges, Methods, and Future Directions
Tian Li
Anit Kumar Sahu
Ameet Talwalkar
Virginia Smith
FedML
129
4,540
0
21 Aug 2019
Scalable and Differentially Private Distributed Aggregation in the
  Shuffled Model
Scalable and Differentially Private Distributed Aggregation in the Shuffled Model
Badih Ghazi
Rasmus Pagh
A. Velingker
FedML
56
98
0
19 Jun 2019
Differentially Private Learning with Adaptive Clipping
Differentially Private Learning with Adaptive Clipping
Galen Andrew
Om Thakkar
H. B. McMahan
Swaroop Ramaswamy
FedML
84
343
0
09 May 2019
Protection Against Reconstruction and Its Applications in Private
  Federated Learning
Protection Against Reconstruction and Its Applications in Private Federated Learning
Abhishek Bhowmick
John C. Duchi
Julien Freudiger
Gaurav Kapoor
Ryan M. Rogers
FedML
85
360
0
03 Dec 2018
Error Compensated Quantized SGD and its Applications to Large-scale
  Distributed Optimization
Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization
Jiaxiang Wu
Weidong Huang
Junzhou Huang
Tong Zhang
81
236
0
21 Jun 2018
cpSGD: Communication-efficient and differentially-private distributed
  SGD
cpSGD: Communication-efficient and differentially-private distributed SGD
Naman Agarwal
A. Suresh
Felix X. Yu
Sanjiv Kumar
H. B. McMahan
FedML
128
491
0
27 May 2018
Towards More Efficient Stochastic Decentralized Learning: Faster
  Convergence and Sparse Communication
Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication
Zebang Shen
Aryan Mokhtari
Tengfei Zhou
P. Zhao
Hui Qian
106
56
0
25 May 2018
Byzantine Stochastic Gradient Descent
Byzantine Stochastic Gradient Descent
Dan Alistarh
Zeyuan Allen-Zhu
Jingkai Li
FedML
73
297
0
23 Mar 2018
GossipGraD: Scalable Deep Learning using Gossip Communication based
  Asynchronous Gradient Descent
GossipGraD: Scalable Deep Learning using Gossip Communication based Asynchronous Gradient Descent
J. Daily
Abhinav Vishnu
Charles Siegel
T. Warfel
Vinay C. Amatya
45
95
0
15 Mar 2018
The Secret Sharer: Evaluating and Testing Unintended Memorization in
  Neural Networks
The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks
Nicholas Carlini
Chang-rui Liu
Ulfar Erlingsson
Jernej Kos
Basel Alomair
150
1,148
0
22 Feb 2018
Differentially Private Federated Learning: A Client Level Perspective
Differentially Private Federated Learning: A Client Level Perspective
Robin C. Geyer
T. Klein
Moin Nabi
FedML
136
1,300
0
20 Dec 2017
Adaptive Consensus ADMM for Distributed Optimization
Adaptive Consensus ADMM for Distributed Optimization
Zheng Xu
Gavin Taylor
Hao Li
Mário A. T. Figueiredo
Xiaoming Yuan
Tom Goldstein
47
62
0
09 Jun 2017
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case
  Study for Decentralized Parallel Stochastic Gradient Descent
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent
Xiangru Lian
Ce Zhang
Huan Zhang
Cho-Jui Hsieh
Wei Zhang
Ji Liu
50
1,233
0
25 May 2017
TensorFlow: A system for large-scale machine learning
TensorFlow: A system for large-scale machine learning
Martín Abadi
P. Barham
Jianmin Chen
Zhiwen Chen
Andy Davis
...
Vijay Vasudevan
Pete Warden
Martin Wicke
Yuan Yu
Xiaoqiang Zhang
GNNAI4CE
433
18,361
0
27 May 2016
Communication-Efficient Learning of Deep Networks from Decentralized
  Data
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
Federated Optimization:Distributed Optimization Beyond the Datacenter
Federated Optimization:Distributed Optimization Beyond the Datacenter
Jakub Konecný
H. B. McMahan
Daniel Ramage
FedML
120
739
0
11 Nov 2015
1