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Federated Learning Classification research benchmarks · SOTA2 Research

Federated Learning Classification

Benchmarks

Dataset NameSOTA MethodSort

Dataset NameSOTA methodMetricTrendResultsLast Updated

MNIST FedAvg 0.962 (test)

96.2Accuracy

88

Feb 26, 2026

77.17SRE

30

Mar 20, 2026

97.7Accuracy

20

Feb 26, 2026

CIFAR-100 non-iid delta=0.3

55.27Accuracy

12

Feb 27, 2026

Tiny-ImageNet IID (test)

36.53Accuracy

12

Feb 27, 2026

Tiny-ImageNet non-iid Dirichlet 0.6 (test)

0.361Accuracy

12

Feb 27, 2026

Tiny-ImageNet non-iid Dirichlet 0.3 (test)

35.7Accuracy

12

Feb 27, 2026

CIFAR-100 IID (test)

56.77Accuracy

12

Feb 27, 2026

CIFAR-100 non-iid Dirichlet 0.6 (test)

55.84Accuracy

12

Feb 27, 2026

EMNIST dir(alpha=0.1) (test)

0.063Max r_k(theta)

10

Apr 1, 2026

CIFAR-10 Late Stage (Rounds 21-50)

62.4Average Accuracy (All)

9

Jul 1, 2026

93.2Label Flip Accuracy

7

Feb 26, 2026

CIFAR-10 FedAvg (test)

65.54Accuracy

6

Feb 26, 2026

FEMNIST FedAvg 0.8235 (test)

81.45Accuracy

4

Feb 26, 2026

Purchase FedAvg 0.922 (test)

92.6Accuracy

4

Feb 26, 2026

CIFAR-100 non-iid Dirichlet 0.3 (test)

—

—Accuracy

0

Feb 26, 2026

Popular datasets

CIFAR-100 CIFAR Tiny-ImageNet MNIST ImageNet

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