Federated learning on non-IID data: A survey
Open accessIntegrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The non-IID training data set in WFL helps to protect the data privacy of the participants.
“The non-IID training data set in WFL helps to protect the data privacy of the participants [160].”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [160]
The citation lists haven’t been fetched yet
References 167 · Cited by 1,062
- Authors
- Zhu, H., Xu, J., Liu, S., & Jin, Y.
- Affiliations
- University of Surrey (GB)
- East China University of Science and Technology (CN)
- Corresponding author
- Jin, Yaochu
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2021-11
- Volume
- 465
- Pages
- 371-390
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W3196371845
- Cited by
- 1,062
- References
- 167
- Citations by year
- 2026: 193 · 2025: 316 · 2024: 274 · 2023: 203 · 2022: 70 · 2021: 5 · 2020: 1
- FWCI
- 48.77
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Neurocomputing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- PUB – Publications at Bielefeld University (Bielefeld University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- federated learning, non-IID data, privacy preservation, horizontal federated learning, vertical federated learning, parametric models, nonparametric models, data heterogeneity, distributed learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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