Who Cited It

Communication-Efficient Learning of Deep Networks from Decentralized Data

2016 · arXiv (Cornell University) · 5,190 citations · 14 from inside this corpus

H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Agüera y Arcas

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

Communication-Efficient Learning of Deep Networks from Decentralized Data (2016)Communication-Efficient Learn…Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challe… (2019)Explainable Artificial Intell…Advances and Open Problems in Federated Learning (2020)Advances and Open Problems in…Practical Secure Aggregation for Privacy-Preserving Machine Learning (2017)Practical Secure Aggregation …Federated Learning in Mobile Edge Networks: A Comprehensive Survey (2020)Federated Learning in Mobile …Federated Learning With Differential Privacy: Algorithms and Performance Analysis (2020)Federated Learning With Diffe…Federated Learning with Non-IID Data (2018)Federated Learning with Non-I…A survey on federated learning (2021)A survey on federated learningComprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference A… (2019)Comprehensive Privacy Analysi…A Joint Learning and Communications Framework for Federated Learning Over Wireless Networ… (2020)A Joint Learning and Communic…Deep Leakage from Gradients (2020)Deep Leakage from GradientsA review of applications in federated learning (2020)A review of applications in f…Federated Learning for Healthcare Informatics (2020)Federated Learning for Health…Model-Contrastive Federated Learning (2021)Model-Contrastive Federated L…A survey on security and privacy of federated learning (2020)A survey on security and priv…
14 of 14 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.
this paper works it cites works citing it node size = global citations · hover for the full title

What cites it, inside the corpus

PaperYearCited
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challe…20199,796
Advances and Open Problems in Federated Learning20205,383
Practical Secure Aggregation for Privacy-Preserving Machine Learning20173,744
Federated Learning in Mobile Edge Networks: A Comprehensive Survey20202,538
Federated Learning With Differential Privacy: Algorithms and Performance Analysis20202,357
Federated Learning with Non-IID Data20181,918
A survey on federated learning20211,847
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference A…20191,624
A Joint Learning and Communications Framework for Federated Learning Over Wireless Networ…20201,541
Deep Leakage from Gradients20201,532
A review of applications in federated learning20201,502
Federated Learning for Healthcare Informatics20201,497
Model-Contrastive Federated Learning20211,485
A survey on security and privacy of federated learning20201,393

Links

DOI · Open access copy · OpenAlex record

Topics

Privacy-Preserving Technologies in DataComputer Science
Human Mobility and Location-Based AnalysisSocial Sciences
Stochastic Gradient Optimization TechniquesComputer Science

Is this record sound?

partial

One field of this record is missing or disagrees with another. What is shown below is what the source publishes.

  • supports5 author record(s) attached.
  • weakensNo references are recorded despite 5,190 citations. A paper this heavily cited did not cite nothing, so the record is incomplete.
  • neutralThe DOI carries no year to check against.
  • supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:44+00:00.

sha256 648f8491aaceaa34…