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 2651–2700 of 6771 papers

TitleStatusHype
Federated Learning-Driven Cybersecurity Framework for IoT Networks with Privacy-Preserving and Real-Time Threat Detection Capabilities—0
Recent Advances in Federated Learning Driven Large Language Models: A Survey on Architecture, Performance, and Security—0
FedCut: A Spectral Analysis Framework for Reliable Detection of Byzantine Colluders—0
Federated Learning Empowered by Generative Content—0
Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence—0
Federated learning, ethics, and the double black box problem in medical AI—0
FedCTTA: A Collaborative Approach to Continual Test-Time Adaptation in Federated Learning—0
Federated Learning for 6G Communications: Challenges, Methods, and Future Directions—0
Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights—0
Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks—0
FedCSD: A Federated Learning Based Approach for Code-Smell Detection—0
Federated Learning for Autoencoder-based Condition Monitoring in the Industrial Internet of Things—0
Communication-Efficient Federated Learning Using Censored Heavy Ball Descent—0
Federated Learning for Blind Image Super-Resolution—0
Federated Learning for Breast Density Classification: A Real-World Implementation—0
Federated Learning for Cellular-connected UAVs: Radio Mapping and Path Planning—0
Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO—0
Federated Learning for Chronic Obstructive Pulmonary Disease Classification with Partial Personalized Attention Mechanism—0
Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches—0
Federated Learning for Coalition Operations—0
Federated Learning for Commercial Image Sources—0
A Secure Aggregation for Federated Learning on Long-Tailed Data—0
Federated Learning for Computationally-Constrained Heterogeneous Devices: A Survey—0
Federated Learning for Computer Vision—0
FedCS: Coreset Selection for Federated Learning—0
Federated Learning for Collaborative Inference Systems: The Case of Early Exit Networks—0
Federated Learning for Coronary Artery Plaque Detection in Atherosclerosis Using IVUS Imaging: A Multi-Hospital Collaboration—0
FedCross: Towards Accurate Federated Learning via Multi-Model Cross-Aggregation—0
Communication Efficient Federated Learning via Ordered ADMM in a Fully Decentralized Setting—0
Federated Learning for Cross-Domain Data Privacy: A Distributed Approach to Secure Collaboration—0
FedCross: Intertemporal Federated Learning Under Evolutionary Games—0
Federated Learning for Data and Model Heterogeneity in Medical Imaging—0
Federated Learning for Data Market: Shapley-UCB for Seller Selection and Incentives—0
Cross-Silo Federated Learning: Challenges and Opportunities—0
Federated Learning for Diabetic Retinopathy Diagnosis: Enhancing Accuracy and Generalizability in Under-Resourced Regions—0
Federated Learning for Diffusion Models—0
FedCRL: Personalized Federated Learning with Contrastive Shared Representations for Label Heterogeneity in Non-IID Data—0
Federated Learning for Distributed Energy-Efficient Resource Allocation—0
Communication Efficient Federated Learning for Generalized Linear Bandits—0
Federated Learning for distribution skewed data using sample weights—0
Federated Learning for Drowsiness Detection in Connected Vehicles—0
Federated Learning for Early Dropout Prediction on Healthy Ageing Applications—0
Federated Learning Forecasting for Strengthening Grid Reliability and Enabling Markets for Resilience—0
Federated Learning for Efficient Condition Monitoring and Anomaly Detection in Industrial Cyber-Physical Systems—0
Federated Learning for Emoji Prediction in a Mobile Keyboard—0
Federated Learning for Energy Constrained IoT devices: A systematic mapping study—0
Efficient Wireless Federated Learning with Partial Model Aggregation—0
Federated Learning for Estimating Heterogeneous Treatment Effects—0
Federated Learning for Face Recognition via Intra-subject Self-supervised Learning—0
A Safe Genetic Algorithm Approach for Energy Efficient Federated Learning in Wireless Communication Networks—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