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 13511400 of 6771 papers

TitleStatusHype
Federated f-Differential PrivacyCode0
Federated Few-shot Learning for Cough Classification with Edge DevicesCode0
Comparative Evaluation of Clustered Federated Learning MethodsCode0
A Survey of Incremental Transfer Learning: Combining Peer-to-Peer Federated Learning and Domain Incremental Learning for Multicenter CollaborationCode0
Federated Fairness Analytics: Quantifying Fairness in Federated LearningCode0
Principled Federated Domain Adaptation: Gradient Projection and Auto-WeightingCode0
CDMA: A Practical Cross-Device Federated Learning Algorithm for General Minimax ProblemsCode0
Federated Face Forgery Detection Learning with Personalized RepresentationCode0
Real World Federated Learning with a Knowledge Distilled Transformer for Cardiac CT ImagingCode0
A generic framework for privacy preserving deep learningCode0
Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous DrivingCode0
Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication ComplexityCode0
Communication-Efficient Zeroth-Order Distributed Online Optimization: Algorithm, Theory, and ApplicationsCode0
Communication-efficient Vertical Federated Learning via Compressed Error FeedbackCode0
Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation SystemCode0
FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing TasksCode0
Federated Continual Learning for Text Classification via Selective Inter-client TransferCode0
Federated Document Visual Question Answering: A Pilot StudyCode0
Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex HullsCode0
FedOS: using open-set learning to stabilize training in federated learningCode0
Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated LearningCode0
Communication Efficient Private Federated Learning Using DitheringCode0
Federated Black-Box Adaptation for Semantic SegmentationCode0
Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning ApplicationsCode0
Communication-Efficient Online Federated Learning Framework for Nonlinear RegressionCode0
UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous DataCode0
A Stochastic Optimization Framework for Private and Fair Learning From Decentralized DataCode0
A Federated Learning Benchmark for Drug-Target InteractionCode0
Fed-ensemble: Improving Generalization through Model Ensembling in Federated LearningCode0
FedQV: Leveraging Quadratic Voting in Federated LearningCode0
A Statistical Analysis of Deep Federated Learning for Intrinsically Low-dimensional DataCode0
Data-Free Diversity-Based Ensemble Selection For One-Shot Federated Learning in Machine Learning Model MarketCode0
FedRec: Federated Learning of Universal Receivers over Fading ChannelsCode0
FedRef: Communication-Efficient Bayesian Fine Tuning with Reference ModelCode0
FedEntropy: Efficient Device Grouping for Federated Learning Using Maximum Entropy JudgmentCode0
Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoTCode0
FedRKG: A Privacy-preserving Federated Recommendation Framework via Knowledge Graph EnhancementCode0
Data Leakage in Federated AveragingCode0
Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced DataCode0
FedDWA: Personalized Federated Learning with Dynamic Weight AdjustmentCode0
Aggregating Intrinsic Information to Enhance BCI Performance through Federated LearningCode0
Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local UpdatesCode0
FedScore: A privacy-preserving framework for federated scoring system developmentCode0
FedDUAL: A Dual-Strategy with Adaptive Loss and Dynamic Aggregation for Mitigating Data Heterogeneity in Federated LearningCode0
FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated LearningCode0
Federated Active Learning for Target Domain GeneralisationCode0
FedSKC: Federated Learning with Non-IID Data via Structural Knowledge CollaborationCode0
FedSkel: Efficient Federated Learning on Heterogeneous Systems with Skeleton Gradients UpdateCode0
Federated clustering with GAN-based data synthesisCode0
Federated Frank-Wolfe AlgorithmCode0
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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