ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2026 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1912.04977
  4. Cited By
Advances and Open Problems in Federated Learning
v1v2v3 (latest)

Advances and Open Problems in Federated Learning

10 December 2019
Peter Kairouz
H. B. McMahan
Brendan Avent
A. Bellet
M. Bennis
A. Bhagoji
Keith Bonawitz
Zachary B. Charles
Graham Cormode
Rachel Cummings
Rafael G. L. DÓliveira
Hubert Eichner
S. E. Rouayheb
David Evans
Josh Gardner
Zachary Garrett
Adria Gascon
Badih Ghazi
Phillip B. Gibbons
Marco Gruteser
Zaïd Harchaoui
Chaoyang He
Lie He
Zhouyuan Huo
Ben Hutchinson
Justin Hsu
Martin Jaggi
T. Javidi
Gauri Joshi
M. Khodak
Jakub Konecný
Aleksandra Korolova
F. Koushanfar
Oluwasanmi Koyejo
Tancrède Lepoint
Yang Liu
Prateek Mittal
M. Mohri
Richard Nock
A. Özgür
Rasmus Pagh
Mariana Raykova
Hang Qi
Daniel Ramage
Ramesh Raskar
Basel Alomair
Weikang Song
Sebastian U. Stich
Ziteng Sun
A. Suresh
Florian Tramèr
Praneeth Vepakomma
Jianyu Wang
Li Xiong
Zheng Xu
Qiang Yang
Felix X. Yu
Han Yu
Sen Zhao
    FedMLAI4CE
ArXiv (abs)PDFHTML

Papers citing "Advances and Open Problems in Federated Learning"

50 / 2,962 papers shown
Differentially Private Secure Multi-Party Computation for Federated
  Learning in Financial Applications
Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applications
David Byrd
Antigoni Polychroniadou
FedML
107
198
0
12 Oct 2020
Federated Learning via Posterior Averaging: A New Perspective and
  Practical Algorithms
Federated Learning via Posterior Averaging: A New Perspective and Practical AlgorithmsInternational Conference on Learning Representations (ICLR), 2020
Maruan Al-Shedivat
Jennifer Gillenwater
Eric Xing
Afshin Rostamizadeh
FedML
332
125
0
11 Oct 2020
Voting-based Approaches For Differentially Private Federated Learning
Voting-based Approaches For Differentially Private Federated Learning
Yuqing Zhu
Xiang Yu
Yi-Hsuan Tsai
Francesco Pittaluga
M. Faraki
Manmohan Chandraker
Yu Wang
FedML
200
26
0
09 Oct 2020
Lower Bounds and Optimal Algorithms for Personalized Federated Learning
Lower Bounds and Optimal Algorithms for Personalized Federated LearningNeural Information Processing Systems (NeurIPS), 2020
Filip Hanzely
Slavomír Hanzely
Samuel Horváth
Peter Richtárik
FedML
285
206
0
05 Oct 2020
How to send a real number using a single bit (and some shared
  randomness)
How to send a real number using a single bit (and some shared randomness)International Colloquium on Automata, Languages and Programming (ICALP), 2020
Ran Ben-Basat
Michael Mitzenmacher
S. Vargaftik
260
20
0
05 Oct 2020
Specialized federated learning using a mixture of experts
Specialized federated learning using a mixture of experts
Edvin Listo Zec
Olof Mogren
John Martinsson
L. R. Sütfeld
D. Gillblad
FedML
224
34
0
05 Oct 2020
Client Selection in Federated Learning: Convergence Analysis and
  Power-of-Choice Selection Strategies
Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies
Yae Jee Cho
Jianyu Wang
Gauri Joshi
FedML
340
499
0
03 Oct 2020
Towards Bidirectional Protection in Federated Learning
Towards Bidirectional Protection in Federated Learning
Lun Wang
Qi Pang
Shuai Wang
Basel Alomair
FedML
223
3
0
02 Oct 2020
Practical One-Shot Federated Learning for Cross-Silo Setting
Practical One-Shot Federated Learning for Cross-Silo SettingInternational Joint Conference on Artificial Intelligence (IJCAI), 2020
Qinbin Li
Bingsheng He
Basel Alomair
FedML
363
151
0
02 Oct 2020
Model-sharing Games: Analyzing Federated Learning Under Voluntary
  Participation
Model-sharing Games: Analyzing Federated Learning Under Voluntary ParticipationAAAI Conference on Artificial Intelligence (AAAI), 2020
Kate Donahue
Jon M. Kleinberg
FedML
275
93
0
02 Oct 2020
STR: Secure Computation on Additive Shares Using the
  Share-Transform-Reveal Strategy
STR: Secure Computation on Additive Shares Using the Share-Transform-Reveal Strategy
Zhihua Xia
Qi Gu
Wenhao Zhou
Lizhi Xiong
J. Weng
Neal N. Xiong
222
0
0
28 Sep 2020
Loosely Coupled Federated Learning Over Generative Models
Loosely Coupled Federated Learning Over Generative Models
Shaoming Song
Yunfeng Shao
Jian Li
FedML
160
1
0
28 Sep 2020
Over-the-Air Federated Learning from Heterogeneous Data
Over-the-Air Federated Learning from Heterogeneous DataIEEE Transactions on Signal Processing (TSP), 2020
Tomer Sery
Stefano Rini
Kobi Cohen
Yonina C. Eldar
FedML
364
215
0
27 Sep 2020
FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated
  Learning
FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning
S. Kadhe
Nived Rajaraman
O. O. Koyluoglu
Kannan Ramchandran
FedML
263
187
0
23 Sep 2020
FedCluster: Boosting the Convergence of Federated Learning via
  Cluster-Cycling
FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling
Cheng Chen
Ziyi Chen
Yi Zhou
B. Kailkhura
FedML
183
66
0
22 Sep 2020
Dynamic Fusion based Federated Learning for COVID-19 Detection
Dynamic Fusion based Federated Learning for COVID-19 DetectionIEEE Internet of Things Journal (IEEE IoT J.), 2020
Weishan Zhang
Tao Zhou
Qinghua Lu
Xiao Wang
Chunsheng Zhu
Haoyun Sun
Zhipeng Wang
Sin Kit Lo
Fei-Yue Wang
FedMLMedIm
190
246
0
22 Sep 2020
Federated Learning for Computational Pathology on Gigapixel Whole Slide
  Images
Federated Learning for Computational Pathology on Gigapixel Whole Slide Images
Ming Y. Lu
Dehan Kong
Jana Lipkova
Richard J. Chen
Rajendra Singh
Drew F. K. Williamson
Tiffany Y. Chen
Faisal Mahmood
FedMLMedIm
297
210
0
21 Sep 2020
Connecting Distributed Pockets of EnergyFlexibility through Federated
  Computations:Limitations and Possibilities
Connecting Distributed Pockets of EnergyFlexibility through Federated Computations:Limitations and PossibilitiesInternational Conference on Machine Learning and Applications (ICMLA), 2020
Javad Mohammadi
J. Thornburg
175
6
0
21 Sep 2020
Training Production Language Models without Memorizing User Data
Training Production Language Models without Memorizing User Data
Swaroop Indra Ramaswamy
Om Thakkar
Rajiv Mathews
Galen Andrew
H. B. McMahan
Franccoise Beaufays
FedML
269
95
0
21 Sep 2020
Private Reinforcement Learning with PAC and Regret Guarantees
Private Reinforcement Learning with PAC and Regret GuaranteesInternational Conference on Machine Learning (ICML), 2020
G. Vietri
Borja Balle
A. Krishnamurthy
Zhiwei Steven Wu
186
68
0
18 Sep 2020
Federated Learning with Nesterov Accelerated Gradient
Federated Learning with Nesterov Accelerated GradientIEEE Transactions on Parallel and Distributed Systems (TPDS), 2020
Zhengjie Yang
Wei Bao
Dong Yuan
Nguyen H. Tran
Albert Y. Zomaya
FedML
283
42
0
18 Sep 2020
Byzantine-Robust Variance-Reduced Federated Learning over Distributed
  Non-i.i.d. Data
Byzantine-Robust Variance-Reduced Federated Learning over Distributed Non-i.i.d. DataInformation Sciences (Inf. Sci.), 2020
Jie Peng
Zhaoxian Wu
Qing Ling
Tianyi Chen
OODFedML
221
28
0
17 Sep 2020
Distilled One-Shot Federated Learning
Distilled One-Shot Federated Learning
Yanlin Zhou
George Pu
Xiyao Ma
Xiaolin Li
D. Wu
FedMLDD
397
176
0
17 Sep 2020
Effective Federated Adaptive Gradient Methods with Non-IID Decentralized
  Data
Effective Federated Adaptive Gradient Methods with Non-IID Decentralized Data
Qianqian Tong
Guannan Liang
J. Bi
FedML
269
28
0
14 Sep 2020
Communication Efficient Distributed Learning with Censored, Quantized,
  and Generalized Group ADMM
Communication Efficient Distributed Learning with Censored, Quantized, and Generalized Group ADMM
Chaouki Ben Issaid
Anis Elgabli
Jihong Park
M. Bennis
Mérouane Debbah
FedML
210
13
0
14 Sep 2020
FLaPS: Federated Learning and Privately Scaling
FLaPS: Federated Learning and Privately ScalingIEEE International Conference on Mobile Adhoc and Sensor Systems (MASS), 2020
Sudipta Paul
Poushali Sengupta
Subhankar Mishra
FedML
132
5
0
13 Sep 2020
Trading Data For Learning: Incentive Mechanism For On-Device Federated
  Learning
Trading Data For Learning: Incentive Mechanism For On-Device Federated LearningGlobal Communications Conference (GLOBECOM), 2020
Rui Hu
Yanmin Gong
FedML
161
74
0
11 Sep 2020
Federated Generalized Bayesian Learning via Distributed Stein
  Variational Gradient Descent
Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient DescentIEEE Transactions on Signal Processing (TSP), 2020
Rahif Kassab
Osvaldo Simeone
FedML
582
50
0
11 Sep 2020
Multi-Central Differential Privacy
Multi-Central Differential Privacy
Thomas Steinke
158
9
0
11 Sep 2020
Local and Central Differential Privacy for Robustness and Privacy in
  Federated Learning
Local and Central Differential Privacy for Robustness and Privacy in Federated LearningNetwork and Distributed System Security Symposium (NDSS), 2020
Mohammad Naseri
Jamie Hayes
Emiliano De Cristofaro
FedML
302
198
0
08 Sep 2020
Blockchain-based Federated Learning for Device Failure Detection in
  Industrial IoT
Blockchain-based Federated Learning for Device Failure Detection in Industrial IoT
Weishan Zhang
Qinghua Lu
Qiuyu Yu
Zhaotong Li
Yue Liu
Sin Kit Lo
Shiping Chen
Xiwei Xu
Liming Zhu
182
7
0
06 Sep 2020
FLFE: A Communication-Efficient and Privacy-Preserving Federated Feature
  Engineering Framework
FLFE: A Communication-Efficient and Privacy-Preserving Federated Feature Engineering Framework
Pei Fang
Zhendong Cai
Hui Chen
Qingjiang Shi
125
6
0
05 Sep 2020
Wireless for Machine Learning
Wireless for Machine Learning
Henrik Hellström
J. M. B. D. Silva
Mohammad Mohammadi Amiri
Mingzhe Chen
Viktoria Fodor
H. Vincent Poor
Carlo Fischione
340
18
0
31 Aug 2020
SEEC: Semantic Vector Federation across Edge Computing Environments
SEEC: Semantic Vector Federation across Edge Computing Environments
Shiqiang Wang
Dean Steuer
Graham A. Bent
N. Desai
FedML
126
2
0
30 Aug 2020
Collaborative Fairness in Federated Learning
Collaborative Fairness in Federated Learning
Lingjuan Lyu
Xinyi Xu
Qian Wang
FedML
170
213
0
27 Aug 2020
Optimization for Supervised Machine Learning: Randomized Algorithms for
  Data and Parameters
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters
Filip Hanzely
212
0
0
26 Aug 2020
Improving Semi-supervised Federated Learning by Reducing the Gradient
  Diversity of Models
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Models
Zhengming Zhang
Yaoqing Yang
Z. Yao
Yujun Yan
Joseph E. Gonzalez
Michael W. Mahoney
FedML
285
39
0
26 Aug 2020
FedCVT: Semi-supervised Vertical Federated Learning with Cross-view
  Training
FedCVT: Semi-supervised Vertical Federated Learning with Cross-view Training
Weijing Chen
Yang Liu
Xinle Liang
FedML
256
67
0
25 Aug 2020
Federated Learning with Communication Delay in Edge Networks
Federated Learning with Communication Delay in Edge Networks
F. Lin
Christopher G. Brinton
Nicolò Michelusi
FedML
97
19
0
21 Aug 2020
Inverse Distance Aggregation for Federated Learning with Non-IID Data
Inverse Distance Aggregation for Federated Learning with Non-IID Data
Yousef Yeganeh
Azade Farshad
Nassir Navab
Shadi Albarqouni
OOD
147
99
0
17 Aug 2020
Siloed Federated Learning for Multi-Centric Histopathology Datasets
Siloed Federated Learning for Multi-Centric Histopathology Datasets
M. Andreux
Jean Ogier du Terrail
C. Béguier
Eric W. Tramel
FedMLOODAI4CE
194
130
0
17 Aug 2020
FLBench: A Benchmark Suite for Federated Learning
FLBench: A Benchmark Suite for Federated Learning
Yuan Liang
Yange Guo
Yanxia Gong
Chunjie Luo
Jianfeng Zhan
Yunyou Huang
FedML
307
11
0
17 Aug 2020
How to Put Users in Control of their Data in Federated Top-N
  Recommendation with Learning to Rank
How to Put Users in Control of their Data in Federated Top-N Recommendation with Learning to Rank
Vito Walter Anelli
Yashar Deldjoo
Tommaso Di Noia
Antonio Ferrara
Fedelucio Narducci
FedML
151
1
0
17 Aug 2020
Shuffled Model of Federated Learning: Privacy, Communication and
  Accuracy Trade-offs
Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs
Antonious M. Girgis
Deepesh Data
Suhas Diggavi
Peter Kairouz
A. Suresh
FedML
187
26
0
17 Aug 2020
Distillation-Based Semi-Supervised Federated Learning for
  Communication-Efficient Collaborative Training with Non-IID Private Data
Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Data
Sohei Itahara
Takayuki Nishio
Yusuke Koda
M. Morikura
Koji Yamamoto
FedML
288
328
0
14 Aug 2020
Dispersed Federated Learning: Vision, Taxonomy, and Future Directions
Dispersed Federated Learning: Vision, Taxonomy, and Future DirectionsIEEE wireless communications (IEEE Wireless Commun.), 2020
L. U. Khan
Walid Saad
Zhu Han
Choong Seon Hong
192
34
0
12 Aug 2020
Holdout SGD: Byzantine Tolerant Federated Learning
Holdout SGD: Byzantine Tolerant Federated Learning
Shahar Azulay
Lior Raz
Amir Globerson
Tomer Koren
Y. Afek
FedML
169
7
0
11 Aug 2020
Federated Learning via Synthetic Data
Federated Learning via Synthetic Data
Jack Goetz
Ambuj Tewari
FedMLDD
131
82
0
11 Aug 2020
Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning
Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning
Sai Praneeth Karimireddy
Martin Jaggi
Satyen Kale
M. Mohri
Sashank J. Reddi
Sebastian U. Stich
A. Suresh
FedML
549
236
0
08 Aug 2020
LotteryFL: Personalized and Communication-Efficient Federated Learning
  with Lottery Ticket Hypothesis on Non-IID Datasets
LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasets
Ang Li
Jingwei Sun
Binghui Wang
Lin Duan
Sicheng Li
Yiran Chen
Xue Yang
FedML
195
147
0
07 Aug 2020
Previous
123...555657585960
Next