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 401450 of 6771 papers

TitleStatusHype
Federated Self-supervised Learning for Video UnderstandingCode1
Federated Self-Training for Semi-Supervised Audio RecognitionCode1
Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech RecognitionCode1
Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and ChallengesCode1
Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked DataCode1
Federated Unlearning: A Survey on Methods, Design Guidelines, and Evaluation MetricsCode1
Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order OptimizationCode1
Deep Federated Learning for Autonomous DrivingCode1
FedFA: Federated Feature AugmentationCode1
FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous DataCode1
A Blockchain-based Decentralized Federated Learning Framework with Committee ConsensusCode1
DeceFL: A Principled Decentralized Federated Learning FrameworkCode1
Adaptive Test-Time Personalization for Federated LearningCode1
FedAUX: Leveraging Unlabeled Auxiliary Data in Federated LearningCode1
A Dynamic Weighted Federated Learning for Android Malware ClassificationCode1
A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime DetectionCode1
DESTRESS: Computation-Optimal and Communication-Efficient Decentralized Nonconvex Finite-Sum OptimizationCode1
Dual-Personalizing Adapter for Federated Foundation ModelsCode1
Device Heterogeneity in Federated Learning: A Superquantile ApproachCode1
Exploring the Vulnerabilities of Federated Learning: A Deep Dive into Gradient Inversion AttacksCode1
Adapt to Adaptation: Learning Personalization for Cross-Silo Federated LearningCode1
Differentially private cross-silo federated learningCode1
DENSE: Data-Free One-Shot Federated LearningCode1
Differentially Private Federated Learning on Heterogeneous DataCode1
Analyzing Federated Learning through an Adversarial LensCode1
FEDIC: Federated Learning on Non-IID and Long-Tailed Data via Calibrated DistillationCode1
Differentially Private Learning with Adaptive ClippingCode1
A Practical Recipe for Federated Learning Under Statistical Heterogeneity Experimental DesignCode1
TARGET: Federated Class-Continual Learning via Exemplar-Free DistillationCode1
Differentially Private Vertical Federated ClusteringCode1
DistFL: Distribution-aware Federated Learning for Mobile ScenariosCode1
Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked VehiclesCode1
Dopamine: Differentially Private Federated Learning on Medical DataCode1
Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platformCode1
Exploring the Distributed Knowledge Congruence in Proxy-data-free Federated DistillationCode1
FedLP: Layer-wise Pruning Mechanism for Communication-Computation Efficient Federated LearningCode1
An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public TransportationCode1
BAFFLE: A Baseline of Backpropagation-Free Federated LearningCode1
FACMIC: Federated Adaptative CLIP Model for Medical Image ClassificationCode1
An Efficient Framework for Clustered Federated LearningCode1
FedMAX: Mitigating Activation Divergence for Accurate and Communication-Efficient Federated LearningCode1
APPFL: Open-Source Software Framework for Privacy-Preserving Federated LearningCode1
FedMedICL: Towards Holistic Evaluation of Distribution Shifts in Federated Medical ImagingCode1
Federated Learning Meets Multi-objective OptimizationCode1
APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a ServiceCode1
Exploiting Shared Representations for Personalized Federated LearningCode1
Edge Federated Learning Via Unit-Modulus Over-The-Air ComputationCode1
Efficient Personalized Federated Learning via Sparse Model-AdaptationCode1
Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class ImbalanceCode1
Applied Federated Learning: Improving Google Keyboard Query SuggestionsCode1
Show:102550
← PrevPage 9 of 136Next →

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