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Personalized Federated Learning research benchmarks · SOTA2 Research

Personalized Federated Learning

Benchmarks

Dataset NameSOTA MethodSort

Dataset NameSOTA methodMetricTrendResultsLast Updated

DRAKE dynamic (Others)

52.56Alast

40

Mar 10, 2026

DRAKE dynamic (Self)

70.67Alast

40

Mar 10, 2026

79.32Accuracy

32

Jun 23, 2026

CIFAR-100 (test)

49.02Accuracy

32

Jun 23, 2026

70.1Alast

30

Mar 10, 2026

DRAKE static (Others)

52.67Alast

20

Mar 10, 2026

85.3Accuracy

13

Feb 26, 2026

64.6Accuracy

13

Feb 26, 2026

62.5Accuracy

13

Feb 26, 2026

81.3Accuracy

13

Feb 26, 2026

42.4Accuracy

13

Feb 26, 2026

CIFAR10 pathological non-IID (500 clients, 80 samples)

90.81Top-1 Acc

11

Feb 26, 2026

CIFAR10 pathological non-IID (100 clients, 400 samples)

93.25Top-1 Acc

11

Feb 26, 2026

CIFAR10 pathological non-IID (50 clients, 800 samples)

94.05Top-1 Acc

11

Feb 26, 2026

MNIST pathological non-IID (500 clients, 96 samples)

0.994Top-1 Acc

11

Feb 26, 2026

MNIST pathological non-IID (100 clients, 480 samples)

99.38Top-1 Acc

11

Feb 26, 2026

MNIST pathological non-IID (50 clients, 960 samples)

99.48Top-1 Acc

11

Feb 26, 2026

DRAKE-Static (Self)

70.41Alast

10

Mar 10, 2026

DRAKE alpha=0.1 Dirichlet non-i.i.d. splits

73.12A_last

10

Mar 10, 2026

DRAKE Dirichlet non-i.i.d. splits alpha=0.5

70.2A_last

10

Mar 10, 2026

DRAKE alpha=1.0 Dirichlet non-i.i.d. splits

69.12A_last

10

Mar 10, 2026

52.4Alast

10

Mar 10, 2026

Fed-LLM Large-Scale (52 clients) (Others)

15.71Alast

9

Mar 10, 2026

Fed-LLM Large-Scale (52 clients) (Self)

14.04Alast

9

Mar 10, 2026

Tiny-ImageNet Pathological 10

261Accuracy@40

9

Feb 27, 2026

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