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 13011350 of 6771 papers

TitleStatusHype
Federated Over-Air Subspace Tracking from Incomplete and Corrupted DataCode0
Federated Learning of Large Models at the Edge via Principal Sub-Model TrainingCode0
A Federated Random Forest Solution for Secure Distributed Machine LearningCode0
A Survey on Group Fairness in Federated Learning: Challenges, Taxonomy of Solutions and Directions for Future ResearchCode0
Federated Causal Inference from Observational DataCode0
Federated Learning with Only Positive LabelsCode0
Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things ApplicationCode0
Federated singular value decomposition for high dimensional dataCode0
Asynchronous Federated OptimizationCode0
Federated Spectral Graph Transformers Meet Neural Ordinary Differential Equations for Non-IID GraphsCode0
Federated Learning in ASR: Not as Easy as You ThinkCode0
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit FeedbackCode0
Federated Learning in Chemical Engineering: A Tutorial on a Framework for Privacy-Preserving Collaboration Across Distributed Data SourcesCode0
Federated Learning Hyper-Parameter Tuning from a System PerspectiveCode0
CorrFL: Correlation-based Neural Network Architecture for Unavailability Concerns in a Heterogeneous IoT EnvironmentCode0
CorrFL: Correlation-Based Neural Network Architecture for Unavailability Concerns in a Heterogeneous IoT EnvironmentCode0
Federated learning framework for collaborative remaining useful life prognostics: an aircraft engine case studyCode0
Federated Learning From Big Data Over NetworksCode0
Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New AlgorithmsCode0
Covariances for Free: Exploiting Mean Distributions for Federated Learning with Pre-Trained ModelsCode0
Federated Learning for Privacy-Preserving Feedforward Control in Multi-Agent SystemsCode0
Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic DataCode0
Federated Learning for Misbehaviour Detection with Variational Autoencoders and Gaussian Mixture ModelsCode0
Federated Learning for Mobile Keyboard PredictionCode0
Federated Learning for Keyword SpottingCode0
Federated Learning for Data StreamsCode0
A Survey on Contribution Evaluation in Vertical Federated LearningCode0
Federated Learning for Non-factorizable Models using Deep Generative Prior ApproximationsCode0
Federated Learning in Unreliable and Resource-Constrained Cellular Wireless NetworksCode0
Federated Learning based on Pruning and RecoveryCode0
Federated Learning Beyond the Star: Local D2D Model Consensus with Global Cluster SamplingCode0
Federated Learning Based Multilingual Emoji Prediction In Clean and Attack ScenariosCode0
Federated Learning Client Pruning for Noisy LabelsCode0
Federated Intelligence for Active Queue Management in Inter-Domain CongestionCode0
Federated Impression for Learning with Distributed Heterogeneous DataCode0
Federated Hypergradient DescentCode0
Federated Learning and Differential Privacy Techniques on Multi-hospital Population-scale Electrocardiogram DataCode0
FedHIL: Heterogeneity Resilient Federated Learning for Robust Indoor Localization with Mobile DevicesCode0
Federated learning compression designed for lightweight communicationsCode0
Real World Federated Learning with a Knowledge Distilled Transformer for Cardiac CT ImagingCode0
Federated Frank-Wolfe AlgorithmCode0
Federated f-Differential PrivacyCode0
Federated Few-shot Learning for Cough Classification with Edge DevicesCode0
Federated Face Forgery Detection Learning with Personalized RepresentationCode0
Federated Fairness Analytics: Quantifying Fairness in Federated LearningCode0
Federated Graph Learning with Structure Proxy AlignmentCode0
Federated Learning Meets Fairness and Differential PrivacyCode0
Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication ComplexityCode0
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
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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