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Classification on FMNIST (test)

99.06AccuracypFedBreD (mg)+FT
80.371285.223190.07594.9269Nov 19, 2022Jun 10, 2023Dec 31, 2023Jul 22, 2024Feb 10, 2025Sep 2, 2025Mar 25, 2026

Evaluation results

MethodLinks
pFedBreD (mg)+FTModel Type=Personalized, Backbone=DNN, Fine-tuning=true2022.1199.06——————
FedAMPModel Type=Personalized, Backbone=DNN2022.1198.88——————
pFedBreD (mg)Model Type=Personalized, Backbone=DNN2022.1198.88——————
FedAMPModel Type=Personalized, Backbone=MCLR2022.1198.72——————
pFedMe+AMModel Type=Personalized, Backbone=DNN, AM=true2022.1198.7——————
pFedMeModel Type=Personalized, Backbone=DNN2022.1198.63——————
pFedMe+FTModel Type=Personalized, Backbone=DNN, Fine-tuning=true2022.1198.63——————
pFedBreD (mg)+FTModel Type=Personalized, Backbone=MCLR, Fine-tuning=true2022.1198.59——————
pFedBreD (mg)Model Type=Personalized, Backbone=MCLR2022.1198.57——————
pFedMeModel Type=Personalized, Backbone=MCLR2022.1197.83——————
pFedMe+AMModel Type=Personalized, Backbone=MCLR, AM=true2022.1197.81——————
pFedMe+FTModel Type=Personalized, Backbone=MCLR, Fine-tuning=true2022.1197.69——————
Per-FedAvgModel Type=Personalized, Backbone=DNN2022.1197.13——————
FedEMModel Type=Global, Backbone=DNN2022.1195.23——————
Per-FedAvgModel Type=Personalized, Backbone=MCLR2022.1193.69——————
FedEMModel Type=Global, Backbone=MCLR2022.1192.47——————
pFedMENumber of Clients=50, Model Type=Personalized2023.0591.9——————
pFedBayesNumber of Clients=50, Model Type=Personalized2023.0591.9——————
pFedVEMNumber of Clients=50, Model Type=Personalized2023.0591.8——————
FedRepNumber of Clients=50, Model Type=Personalized2023.0591.5——————
FedPerNumber of Clients=50, Model Type=Personalized2023.0591.4——————
pFedMENumber of Clients=100, Model Type=Personalized2023.0591.4——————
pFedVEMNumber of Clients=100, Model Type=Personalized2023.0591.4——————
pFedBayesNumber of Clients=100, Model Type=Personalized2023.0591.3——————
FedPerNumber of Clients=100, Model Type=Personalized2023.0590.7——————
FedRepNumber of Clients=100, Model Type=Personalized2023.0590.7——————
pFedVEMNumber of Clients=200, Model Type=Personalized2023.0590.7——————
pFedMENumber of Clients=200, Model Type=Personalized2023.0590.6——————
pFedBayesNumber of Clients=200, Model Type=Personalized2023.0590.5——————
SFLModel=CNN, C=2, K=52023.1189.91——————
FedRepNumber of Clients=200, Model Type=Personalized2023.0589.9——————
FedPerNumber of Clients=200, Model Type=Personalized2023.0589.7——————
SFLModel=CNN, C=2, K=202023.1189.6——————
LocalNumber of Clients=50, Model Type=Personalized2023.0589.2——————
PFLModel=CNN, C=2, K=202023.1189.16——————
PFLModel=CNN, C=2, K=502023.1189.1——————
SFLModel=CNN, C=2, K=502023.1189.05——————
PerFedavgNumber of Clients=50, Model Type=Personalized2023.0588.7——————
PFLModel=CNN, C=2, K=52023.1188.61——————
PerFedavgNumber of Clients=100, Model Type=Personalized2023.0588.6——————
PerFedavgNumber of Clients=200, Model Type=Personalized2023.0588.3——————
SFLModel=CNN, C=1, K=52023.1188.03——————
FedAFalpha=0.12024.0487.91——————
FedAFalpha=0.022024.0487.53——————
LocalNumber of Clients=100, Model Type=Personalized2023.0587.5——————
FedAFalpha=0.052024.0487.29——————
SFLModel=CNN, C=1, K=202023.1186.75——————
FedDMalpha=0.12024.0486.65——————
PFLModel=CNN, C=1, K=52023.1186.61——————
PFLModel=LeNet-5, C=2, K=502023.1186.58——————
SFLModel=LeNet-5, C=2, K=52023.1186.52——————
FedProxNumber of Clients=200, Model Type=Global2023.0586.5——————
PFLModel=LeNet-5, C=2, K=202023.1186.35——————
FedProxNumber of Clients=100, Model Type=Global2023.0586.3——————
Co-Boostingbeta=0.52023.0986.21——————
pFedVEMNumber of Clients=200, Model Type=Global2023.0586.2——————
IFCANumber of Clients=200, Model Type=Personalized2023.0586.1——————
FedDMalpha=0.052024.0486.08——————
FedAvgNumber of Clients=200, Model Type=Global2023.0585.9——————
PFLModel=LeNet-5, C=2, K=52023.1185.86——————
LocalNumber of Clients=200, Model Type=Personalized2023.0585.7——————
ScaffoldNumber of Clients=50, Model Type=Global2023.0585.6——————
IFCANumber of Clients=100, Model Type=Personalized2023.0585.6——————
pFedVEMNumber of Clients=100, Model Type=Global2023.0585.6——————
SFLModel=CNN, C=1, K=502023.1185.44——————
FedAvgNumber of Clients=100, Model Type=Global2023.0585.4——————
ScaffoldNumber of Clients=100, Model Type=Global2023.0585.4——————
PFLModel=CNN, C=1, K=202023.1185.4——————
FedDMalpha=0.022024.0485.36——————
pFedMENumber of Clients=200, Model Type=Global2023.0585.1——————
FedProxNumber of Clients=50, Model Type=Global2023.0584.8——————
FedDynalpha=0.12024.0484.73——————
SFLModel=LeNet-5, C=2, K=202023.1184.69——————
PFLModel=CNN, C=1, K=502023.1184.62——————
ScaffoldNumber of Clients=200, Model Type=Global2023.0584.6——————
pFedMENumber of Clients=100, Model Type=Global2023.0584.4——————
pFedBayesNumber of Clients=200, Model Type=Global2023.0584.4——————
pFedBayesNumber of Clients=100, Model Type=Global2023.0584.2——————
IFCANumber of Clients=50, Model Type=Personalized2023.0584.1——————
SFLModel=LeNet-5, C=1, K=52023.1183.97——————
pFedVEMNumber of Clients=50, Model Type=Global2023.0583.9——————
FuseFLalpha=0.05, K=42024.1083.65——————
SFLModel=LeNet-5, C=2, K=502023.1183.58——————
FedAvgNumber of Clients=50, Model Type=Global2023.0583.5——————
pFedBayesNumber of Clients=50, Model Type=Global2023.0583.5——————
Co-Boostingbeta=0.32023.0983.37——————
FuseFLalpha=0.05, K=22024.1083.23——————
FedAvg+AMModel Type=Global, Backbone=MCLR, AM=true2022.1183.04——————
FedAvgModel Type=Global, Backbone=MCLR2022.1182.9——————
PFLModel=LeNet-5, C=1, K=52023.1182.57——————
pFedMeModel Type=Global, Backbone=MCLR2022.1182.3——————
FedGenalpha=0.12024.0482.29——————
FedAvgalpha=0.12024.0482.19——————
pFedMENumber of Clients=50, Model Type=Global2023.0582——————
FedAvg+AMModel Type=Global, Backbone=DNN, AM=true2022.1181.93——————
TsetlinWiSARD-300Platform=XC7Z020, Algorithm=WiSARD, Purpose=Training2026.0381.87——————
MOONalpha=0.12024.0481.61——————
FedProxalpha=0.12024.0481.56——————
FedAvgModel Type=Global, Backbone=DNN2022.1181.22——————
PFLModel=LeNet-5, C=1, K=202023.1181.09——————