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 11011150 of 6771 papers

TitleStatusHype
Federated Stain Normalization for Computational PathologyCode0
BAFFLE : Blockchain Based Aggregator Free Federated LearningCode0
A Joint Approach to Local Updating and Gradient Compression for Efficient Asynchronous Federated LearningCode0
Federated Over-Air Subspace Tracking from Incomplete and Corrupted DataCode0
Federated Optimization for Heterogeneous NetworksCode0
Federated Prediction-Powered Inference from Decentralized DataCode0
Federated Neural Radiance FieldsCode0
Adaptive Gradient-Based Meta-Learning MethodsCode0
Federated Nearest Neighbor Classification with a Colony of Fruit-Flies: With SupplementCode0
Federated Noisy Client LearningCode0
FedPCL-CDR: A Federated Prototype-based Contrastive Learning Framework for Privacy-Preserving Cross-domain RecommendationCode0
Federated Submodel Optimization for Hot and Cold Data FeaturesCode0
Federated Multi-Task LearningCode0
Federated Multimodal Learning with Dual Adapters and Selective Pruning for Communication and Computational EfficiencyCode0
Federated Multi-armed Bandits with PersonalizationCode0
Federated Multi-Task Learning on Non-IID Data Silos: An Experimental StudyCode0
Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer InterfacesCode0
Federated Machine Learning: Concept and ApplicationsCode0
Federated Low-Rank Adaptation for Foundation Models: A SurveyCode0
Federated Learning with Uncertainty-Based Client Clustering for Fleet-Wide Fault DiagnosisCode0
Federated Learning with Unreliable Clients: Performance Analysis and Mechanism DesignCode0
Backdoor Attack is a Devil in Federated GAN-based Medical Image SynthesisCode0
Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often!Code0
Federated Learning with Reduced Information Leakage and ComputationCode0
A Whole-Process Certifiably Robust Aggregation Method Against Backdoor Attacks in Federated LearningCode0
Federated Learning with Reservoir State Analysis for Time Series Anomaly DetectionCode0
Federated LoRA with Sparse CommunicationCode0
Federated Survival ForestsCode0
Adaptive Federated Learning with Auto-Tuned ClientsCode0
A Wasserstein Minimax Framework for Mixed Linear RegressionCode0
Labeling Chaos to Learning Harmony: Federated Learning with Noisy LabelsCode0
Federated Learning With Individualized Privacy Through Client SamplingCode0
Federated Learning for Time-Series Healthcare Sensing with Incomplete ModalitiesCode0
Federated Learning with Intermediate Representation RegularizationCode0
Federated Learning with Heterogeneous Data Handling for Robust Vehicular Object DetectionCode0
Federated Learning with Non-IID DataCode0
A Hybrid Approach to Privacy-Preserving Federated LearningCode0
A V2X-based Privacy Preserving Federated Measuring and Learning SystemCode0
3FM: Multi-modal Meta-learning for Federated TasksCode0
Auto-weighted Robust Federated Learning with Corrupted Data SourcesCode0
Federated Learning with a Single Shared ImageCode0
Federated Learning with Additional Mechanisms on Clients to Reduce Communication CostsCode0
FedTune: Automatic Tuning of Federated Learning Hyper-Parameters from System PerspectiveCode0
Automatic Structured Pruning for Efficient Architecture in Federated LearningCode0
Adaptive Federated Learning in Resource Constrained Edge Computing SystemsCode0
Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning FrameworkCode0
Federated Learning with Only Positive LabelsCode0
Federated Hybrid Model Pruning through Loss Landscape ExplorationCode0
Federated Learning via Consensus Mechanism on Heterogeneous Data: A New Perspective on ConvergenceCode0
Federated Learning via Plurality VoteCode0
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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