SOTAVerified

Federated Learning

Federated Learning is a machine learning approach that allows multiple devices or entities to collaboratively train a shared model without exchanging their data with each other. Instead of sending data to a central server for training, the model is trained locally on each device, and only the model updates are sent to the central server, where they are aggregated to improve the shared model.

This approach allows for privacy-preserving machine learning, as each device keeps its data locally and only shares the information needed to improve the model.

Papers

Showing 64016450 of 6771 papers

TitleStatusHype
Embedding-Based Federated Data Sharing via Differentially Private Conditional VAEsCode0
A Client-server Deep Federated Learning for Cross-domain Surgical Image SegmentationCode0
REFOL: Resource-Efficient Federated Online Learning for Traffic Flow ForecastingCode0
3FM: Multi-modal Meta-learning for Federated TasksCode0
Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma SegmentationCode0
Multi-Level Additive Modeling for Structured Non-IID Federated LearningCode0
Efficient Vertical Federated Learning Method for Ridge Regression of Large-Scale Samples via Least-Squares SolutionCode0
Federated Learning Client Pruning for Noisy LabelsCode0
Federated Learning Beyond the Star: Local D2D Model Consensus with Global Cluster SamplingCode0
GQFedWAvg: Optimization-Based Quantized Federated Learning in General Edge Computing SystemsCode0
Covariances for Free: Exploiting Mean Distributions for Federated Learning with Pre-Trained ModelsCode0
Regularizing and Aggregating Clients with Class Distribution for Personalized Federated LearningCode0
Gradient Coreset for Federated LearningCode0
SemiSFL: Split Federated Learning on Unlabeled and Non-IID DataCode0
A Federated Random Forest Solution for Secure Distributed Machine LearningCode0
Federated Learning based on Pruning and RecoveryCode0
Adaptive Gradient-Based Meta-Learning MethodsCode0
CorrFL: Correlation-Based Neural Network Architecture for Unavailability Concerns in a Heterogeneous IoT EnvironmentCode0
Multi-Modal Federated Learning for Cancer Staging over Non-IID Datasets with Unbalanced ModalitiesCode0
Supernet Training for Federated Image Classification under System HeterogeneityCode0
Gradient Inversion Attacks on Parameter-Efficient Fine-TuningCode0
Efficient Federated Learning against Heterogeneous and Non-stationary Client UnavailabilityCode0
Federated Learning Based Multilingual Emoji Prediction In Clean and Attack ScenariosCode0
Gradient Leakage Defense with Key-Lock Module for Federated LearningCode0
Federated Learning and Differential Privacy Techniques on Multi-hospital Population-scale Electrocardiogram DataCode0
Multimodal Federated Learning with Missing Modality via Prototype Mask and ContrastCode0
Multimodal Federated Learning With Missing Modalities through Feature Imputation NetworkCode0
TS-Inverse: A Gradient Inversion Attack Tailored for Federated Time Series Forecasting ModelsCode0
Client-specific Property Inference against Secure Aggregation in Federated LearningCode0
Client Selection for Federated Learning with Heterogeneous Resources in Mobile EdgeCode0
Client Recruitment for Federated Learning in ICU Length of Stay PredictionCode0
CDMA: A Practical Cross-Device Federated Learning Algorithm for General Minimax ProblemsCode0
Gradients Stand-in for Defending Deep Leakage in Federated LearningCode0
CorrFL: Correlation-based Neural Network Architecture for Unavailability Concerns in a Heterogeneous IoT EnvironmentCode0
Federated Intelligence for Active Queue Management in Inter-Domain CongestionCode0
FedBChain: A Blockchain-enabled Federated Learning Framework for Improving DeepConvLSTM with Comparative Strategy InsightsCode0
Suppressing Poisoning Attacks on Federated Learning for Medical ImagingCode0
Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means ClusteringCode0
GRAIN: Exact Graph Reconstruction from GradientsCode0
Convergence Analysis of Sequential Federated Learning on Heterogeneous DataCode0
Reliable Vertical Federated Learning in 5G Core Network ArchitectureCode0
From Isolation to Collaboration: Federated Class-Heterogeneous Learning for Chest X-Ray ClassificationCode0
Graph Federated Learning for CIoT Devices in Smart Home ApplicationsCode0
Bayesian Nonparametric Federated Learning of Neural NetworksCode0
Controlling Participation in Federated Learning with FeedbackCode0
Federated Impression for Learning with Distributed Heterogeneous DataCode0
PPIDSG: A Privacy-Preserving Image Distribution Sharing Scheme with GAN in Federated LearningCode0
Federated Hypergradient DescentCode0
Federated Graph Learning with Structure Proxy AlignmentCode0
Semi-Federated LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SiloBN + ASAMmIoU49.75Unverified
2SiloBN + SAMmIoU49.1Unverified
3SiloBNmIoU45.96Unverified
4FedSAM + SWAmIoU43.42Unverified
5FedASAM + SWAmIoU43.02Unverified
6FedAvg + SWAmIoU42.48Unverified
7FedASAMmIoU42.27Unverified
8FedSAMmIoU41.22Unverified
9FedAvgmIoU38.65Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAAcc@1-1262Clients68.32Unverified
2FedSAM + SWAAcc@1-1262Clients68.12Unverified
3FedAvg + SWAAcc@1-1262Clients67.52Unverified
4FedASAMAcc@1-1262Clients64.23Unverified
5FedSAMAcc@1-1262Clients63.72Unverified
6FedAvgAcc@1-1262Clients61.91Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.64Unverified
2FedASAMACC@1-100Clients39.76Unverified
3FedSAM + SWAACC@1-100Clients39.51Unverified
4FedSAMACC@1-100Clients36.93Unverified
5FedAvgACC@1-100Clients36.74Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients41.62Unverified
2FedASAMACC@1-100Clients40.81Unverified
3FedSAM + SWAACC@1-100Clients39.24Unverified
4FedAvgACC@1-100Clients38.59Unverified
5FedSAMACC@1-100Clients38.56Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.72Unverified
2FedSAM + SWAACC@1-100Clients46.76Unverified
3FedASAMACC@1-100Clients46.58Unverified
4FedSAMACC@1-100Clients44.84Unverified
5FedAvgACC@1-100Clients41.27Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.27Unverified
2FedASAMACC@1-100Clients47.78Unverified
3FedSAM + SWAACC@1-100Clients46.47Unverified
4FedSAMACC@1-100Clients46.05Unverified
5FedAvgACC@1-100Clients42.17Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients49.17Unverified
2FedSAM + SWAACC@1-100Clients47.96Unverified
3FedASAMACC@1-100Clients45.61Unverified
4FedSAMACC@1-100Clients44.73Unverified
5FedAvgACC@1-100Clients40.43Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.01Unverified
2FedSAM + SWAACC@1-100Clients39.3Unverified
3FedASAMACC@1-100Clients36.04Unverified
4FedSAMACC@1-100Clients31.04Unverified
5FedAvgACC@1-100Clients30.25Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.97Unverified
2FedASAM + SWAACC@1-100Clients54.79Unverified
3FedSAM + SWAACC@1-100Clients53.67Unverified
4FedSAMACC@1-100Clients53.39Unverified
5FedAvgACC@1-100Clients50.25Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.5Unverified
2FedSAM + SWAACC@1-100Clients54.36Unverified
3FedASAM + SWAACC@1-100Clients54.1Unverified
4FedSAMACC@1-100Clients53.97Unverified
5FedAvgACC@1-100Clients50.66Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.81Unverified
2FedSAMACC@1-100Clients54.01Unverified
3FedSAM + SWAACC@1-100Clients53.9Unverified
4FedASAM + SWAACC@1-100Clients53.86Unverified
5FedAvgACC@1-100Clients49.92Unverified
#ModelMetricClaimedVerifiedStatus
1AdaBestAverage Top-1 Accuracy56.2Unverified