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 5401–5450 of 6771 papers

TitleStatusHype
Boosting Federated Learning with FedEntOpt: Mitigating Label Skew by Entropy-Based Client Selection—0
Optimizing Federated Learning for Medical Image Classification on Distributed Non-iid Datasets with Partial Labels—0
Optimizing Federated Learning in LEO Satellite Constellations via Intra-Plane Model Propagation and Sink Satellite Scheduling—0
Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks—0
Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learning—0
Optimizing Quantum Federated Learning Based on Federated Quantum Natural Gradient Descent—0
Optimizing Resource-Efficiency for Federated Edge Intelligence in IoT Networks—0
Robust Federated Learning against both Data Heterogeneity and Poisoning Attack via Aggregation Optimization—0
Optimizing Split Points for Error-Resilient SplitFed Learning—0
Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systems—0
Order Optimal Bounds for One-Shot Federated Learning over non-Convex Loss Functions—0
Orthogonal Calibration for Asynchronous Federated Learning—0
Overcoming Catastrophic Forgetting in Federated Class-Incremental Learning via Federated Global Twin Generator—0
Overcoming Challenges of Partial Client Participation in Federated Learning : A Comprehensive Review—0
Overcoming Forgetting in Federated Learning on Non-IID Data—0
Overcoming label shift in targeted federated learning—0
Overlay-based Decentralized Federated Learning in Bandwidth-limited Networks—0
Overpredictive Signal Analytics in Federated Learning: Algorithms and Analysis—0
Over-the-Air Aggregation for Federated Learning: Waveform Superposition and Prototype Validation—0
Over-The-Air Clustered Wireless Federated Learning—0
Over-the-Air Computation Aided Federated Learning with the Aggregation of Normalized Gradient—0
Over-the-Air Decentralized Federated Learning—0
Over-the-Air Federated Averaging with Limited Power and Privacy Budgets—0
Over-the-Air Federated Edge Learning with Hierarchical Clustering—0
Over-the-Air Federated Learning and Optimization—0
Over-the-Air Federated Learning In Broadband Communication—0
Over-the-Air Federated Learning in Cell-Free MIMO with Long-term Power Constraint—0
Over-the-Air Federated Learning in Satellite systems—0
Over-the-Air Federated Learning Over MIMO Channels: A Sparse-Coded Multiplexing Approach—0
Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMO—0
Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions—0
Over-The-Air Federated Learning under Byzantine Attacks—0
Over-the-Air Federated Learning via Weighted Aggregation—0
Over-the-Air Federated Learning with Retransmissions (Extended Version)—0
Over-the-Air Federated Learning with Compressed Sensing: Is Sparsification Necessary?—0
Over-the-Air Federated Learning with Joint Adaptive Computation and Power Control—0
Over-the-Air Federated Learning with Phase Noise: Analysis and Countermeasures—0
Over-the-Air Federated Learning with Privacy Protection via Correlated Additive Perturbations—0
Over-the-Air Federated Multi-Task Learning Over MIMO Multiple Access Channels—0
P3LS: Partial Least Squares under Privacy Preservation—0
P4L: Privacy Preserving Peer-to-Peer Learning for Infrastructureless Setups—0
PackVFL: Efficient HE Packing for Vertical Federated Learning—0
PAFedFV: Personalized and Asynchronous Federated Learning for Finger Vein Recognition—0
PAGE: Equilibrate Personalization and Generalization in Federated Learning—0
Papaya: Federated Learning, but Fully Decentralized—0
Papaya: Practical, Private, and Scalable Federated Learning—0
Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator—0
Parallel Restarted SPIDER -- Communication Efficient Distributed Nonconvex Optimization with Optimal Computation Complexity—0
Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks—0
Parameter-Efficient Transfer Learning under Federated Learning for Automatic Speech Recognition—0
Show:102550
← PrevPage 109 of 136Next →
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