Who Cited It

Federated Learning with Non-IID Data

2018 · arXiv (Cornell University) · 1,918 citations · 5 from inside this corpus

Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda low, Damon Civin low, Vikas Chandra

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.

Federated Learning with Non-IID Data (2018)Federated Learning with Non-I…[No title in the source record — DROPS (Schloss Dagstuhl – Leibniz Center for Informatics… (2015)[No title in the source recor…Adaptive Subgradient Methods for Online Learning and Stochastic Optimization (2010)Adaptive Subgradient Methods …Large-Scale Machine Learning with Stochastic Gradient Descent (2010)Large-Scale Machine Learning …Communication-Efficient Learning of Deep Networks from Decentralized Data (2016)Communication-Efficient Learn…Practical Secure Aggregation for Privacy-Preserving Machine Learning (2017)Practical Secure Aggregation …Federated Learning: Strategies for Improving Communication Efficiency (2016)Federated Learning: Strategie…Federated Machine Learning (2019)Federated Machine LearningFederated Learning in Mobile Edge Networks: A Comprehensive Survey (2020)Federated Learning in Mobile …Adaptive Federated Learning in Resource Constrained Edge Computing Systems (2019)Adaptive Federated Learning i…Federated learning in medicine: facilitating multi-institutional collaborations without s… (2020)Federated learning in medicin…Federated Learning for Healthcare Informatics (2020)Federated Learning for Health…
11 of 11 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 this paper cites, inside the corpus

PaperYearCited
[No title in the source record — DROPS (Schloss Dagstuhl – Leibniz Center for Informatics…201550,396
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization20108,621
Large-Scale Machine Learning with Stochastic Gradient Descent20105,695
Communication-Efficient Learning of Deep Networks from Decentralized Data20165,190
Practical Secure Aggregation for Privacy-Preserving Machine Learning20173,744
Federated Learning: Strategies for Improving Communication Efficiency20163,050

What cites it, inside the corpus

PaperYearCited
Federated Machine Learning20196,228
Federated Learning in Mobile Edge Networks: A Comprehensive Survey20202,538
Adaptive Federated Learning in Resource Constrained Edge Computing Systems20192,293
Federated learning in medicine: facilitating multi-institutional collaborations without s…20201,575
Federated Learning for Healthcare Informatics20201,497

Links

DOI · Open access copy · OpenAlex record

Topics

Privacy-Preserving Technologies in DataComputer Science
Mobile Crowdsensing and CrowdsourcingComputer Science
Stochastic Gradient Optimization TechniquesComputer Science

Is this record sound?

complete

Nothing in this record contradicts itself and no field we check is missing.

  • supports6 author record(s) attached.
  • supports24 reference(s) recorded.
  • neutralThe DOI carries no year to check against.
  • supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:52+00:00.

sha256 930f5bc64fea8a60…