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A Performance Evaluation of Federated Learning Algorithms
Open accessAbstract
Federated learning is an approach to distributed machine learning where a global model is learned by aggregating models that have been trained locally on data-generating clients. Contrary to centralized optimization, clients can be very large in number and face challenges of data and network heterogeneity. Examples of clients include smartphones and connected vehicles, which highlights the practical relevance of federated learning. We benchmark three federated learning algorithms and compare their performance against a centralized approach where data resides on the server. The algorithms Federated Averaging (FedAvg), Federated Stochastic Variance Reduced Gradient, and CO-OP are evaluated on the MNIST dataset, using both i.i.d. and non-i.i.d. partitionings of the data. Our results show that FedAvg achieves the highest accuracy among the federated algorithms, regardless of how data was partitioned. Our comparison between FedAvg and centralized learning shows that they are practically equivalent when i.i.d. data is used. However, the centralized approach outperforms FedAvg with non-i.i.d. data.
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In the citing paper
“Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates.”
From Federated learning enables big data for rare cancer boundary detection (2022) · cites this paper as [18]
In the citing paper
“These algorithms were evaluated on MINIST dataset with the use of non-i.i.d. and i.i.d.partitioning of data.”
From Applications of Federated Learning; Taxonomy, Challenges, and Research Trends (2022) · cites this paper as [125]
In the citing paper
“Nilsson et al. benchmarked three FL algorithms.They compared the performance of these three algorithms by residing the data on the server [125].The algorithms include federated averaging (FedAvg), CO-OP, and Federated-Stochastic Variance Reduced Gradient.”
From Applications of Federated Learning; Taxonomy, Challenges, and Research Trends (2022) · cites this paper as [125]
In the citing paper
“The research resulted in FedAvg as the highest accuracy algorithm among all.”
From Applications of Federated Learning; Taxonomy, Challenges, and Research Trends (2022) · cites this paper as [125]
In the citing paper
“FedAvg achieves the highest accuracy of 98% among these algorithms, regardless of the manner of data partitioning.”
From Applications of Federated Learning; Taxonomy, Challenges, and Research Trends (2022) · cites this paper as [125]
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Metadata
- Authors
- Nilsson, A., Smith, S., Ulm, G., Gustavsson, E., & Jirstrand, M.
- Affiliations
- Fraunhofer Chalmers Research Centre for Industrial Mathematics (SE)
- Venue
- Proceedings of the Second Workshop on Distributed Infrastructures for Deep Learning
- Publisher
- ACM
- Published
- 2018-12-10
- Pages
- 1-8
- OpenAlex
- W2900594532
- Cited by
- 328
- References
- 12
- Citations by year
- 2026: 31 · 2025: 52 · 2024: 80 · 2023: 68 · 2022: 51 · 2021: 24 · 2020: 15 · 2019: 7
- FWCI
- 4.68
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Chalmers Research (Chalmers University of Technology) (submittedVersion)
- Funders
- VINNOVA (2016-04260)
- 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, FedAvg, co-ops, centralized learning, non-IID data, data heterogeneity, network heterogeneity, MNIST, 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.
What counts as a claim ›How we measure confidence ›How we check this ›
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