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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3201–3250 of 6771 papers

TitleStatusHype
Federated Learning for Internet of Things: Applications, Challenges, and Opportunities—0
CYCle: Choosing Your Collaborators Wisely to Enhance Collaborative Fairness in Decentralized Learning—0
A Thorough Assessment of the Non-IID Data Impact in Federated Learning—0
Training Fair Models in Federated Learning without Data Privacy Infringement—0
FedFeat+: A Robust Federated Learning Framework Through Federated Aggregation and Differentially Private Feature-Based Classifier Retraining—0
Federated Learning for Inference at Anytime and Anywhere—0
Federated Learning for Industrial Internet of Things in Future Industries—0
Cybersecurity Threats in Connected and Automated Vehicles based Federated Learning Systems—0
FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning—0
Federated Learning for ICD Classification with Lightweight Models and Pretrained Embeddings—0
Federated Learning for Healthcare Informatics—0
FedFMC: Sequential Efficient Federated Learning on Non-iid Data—0
A Theoretical Analysis of Efficiency Constrained Utility-Privacy Bi-Objective Optimization in Federated Learning—0
SGDE: Secure Generative Data Exchange for Cross-Silo Federated Learning—0
Adaptive Deadline and Batch Layered Synchronized Federated Learning—0
FedFNN: Faster Training Convergence Through Update Predictions in Federated Recommender Systems—0
Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges—0
FedFog: Network-Aware Optimization of Federated Learning over Wireless Fog-Cloud Systems—0
Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning—0
Federated Learning for Face Recognition with Gradient Correction—0
Federated Learning for Face Recognition via Intra-subject Self-supervised Learning—0
Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning—0
Fed-FSNet: Mitigating Non-I.I.D. Federated Learning via Fuzzy Synthesizing Network—0
A Theorem of the Alternative for Personalized Federated Learning—0
Federated Learning for Estimating Heterogeneous Treatment Effects—0
Efficient Wireless Federated Learning with Partial Model Aggregation—0
CSAFL: A Clustered Semi-Asynchronous Federated Learning Framework—0
FedGA: A Fair Federated Learning Framework Based on the Gini Coefficient—0
FedGA: Federated Learning with Gradient Alignment for Error Asymmetry Mitigation—0
FedGAI: Federated Style Learning with Cloud-Edge Collaboration for Generative AI in Fashion Design—0
Federated Learning for Energy Constrained IoT devices: A systematic mapping study—0
FedGBF: An efficient vertical federated learning framework via gradient boosting and bagging—0
FedGCA: Global Consistent Augmentation Based Single-Source Federated Domain Generalization—0
Federated Learning for Emoji Prediction in a Mobile Keyboard—0
CRS-FL: Conditional Random Sampling for Communication-Efficient and Privacy-Preserving Federated Learning—0
A Systematic Review of Federated Generative Models—0
FedGen: Generalizable Federated Learning for Sequential Data—0
FedGeo: Privacy-Preserving User Next Location Prediction with Federated Learning—0
A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates—0
Federated Learning for Efficient Condition Monitoring and Anomaly Detection in Industrial Cyber-Physical Systems—0
Federated Learning Forecasting for Strengthening Grid Reliability and Enabling Markets for Resilience—0
FedGL: Federated Graph Learning Framework with Global Self-Supervision—0
CRSFL: Cluster-based Resource-aware Split Federated Learning for Continuous Authentication—0
FedGlu: A personalized federated learning-based glucose forecasting algorithm for improved performance in glycemic excursion regions—0
FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation—0
FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning—0
Federated Learning for Early Dropout Prediction on Healthy Ageing Applications—0
Federated Learning for Drowsiness Detection in Connected Vehicles—0
FedGradNorm: Personalized Federated Gradient-Normalized Multi-Task Learning—0
Cross-Training with Multi-View Knowledge Fusion for Heterogenous Federated Learning—0
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All datasetsCityscapes heterogeneousLandmarks-User-160kCIFAR-100 (alpha=0, 10 clients per round)CIFAR-100 (alpha=0, 20 clients per round)CIFAR-100 (alpha=0.5, 10 clients per round)CIFAR-100 (alpha=0.5, 20 clients per round)CIFAR-100 (alpha=0.5, 5 clients per round)CIFAR-100 (alpha=0, 5 clients per round)CIFAR-100 (alpha=1000, 10 clients per round)CIFAR-100 (alpha=1000, 20 clients per round)CIFAR-100 (alpha=1000, 5 clients per round)CIFAR100 (alpha=0.3, 10 clients per round)

Benchmark Results

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