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 2001–2050 of 6771 papers

TitleStatusHype
Entity Augmentation for Efficient Classification of Vertically Partitioned Data with Limited Overlap—0
Entity Resolution and Federated Learning get a Federated Resolution—0
A Theorem of the Alternative for Personalized Federated Learning—0
EPIC: Enhancing Privacy through Iterative Collaboration—0
Epidemic Decision-making System Based Federated Reinforcement Learning—0
Centroid Approximation for Byzantine-Tolerant Federated Learning—0
CSAFL: A Clustered Semi-Asynchronous Federated Learning Framework—0
CRS-FL: Conditional Random Sampling for Communication-Efficient and Privacy-Preserving Federated Learning—0
Equitable Federated Learning with Activation Clustering—0
Equitable-FL: Federated Learning with Sparsity for Resource-Constrained Environment—0
Escaping Data Scarcity for High-Resolution Heterogeneous Face Hallucination—0
Escaping Saddle Points in Distributed Newton's Method with Communication Efficiency and Byzantine Resilience—0
Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression—0
Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning—0
A Systematic Review of Federated Generative Models—0
A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates—0
CRSFL: Cluster-based Resource-aware Split Federated Learning for Continuous Authentication—0
Estimation of Individual Device Contributions for Incentivizing Federated Learning—0
Cross-Training with Multi-View Knowledge Fusion for Heterogenous Federated Learning—0
E-Tree Learning: A Novel Decentralized Model Learning Framework for Edge AI—0
Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning—0
CG-FedLLM: How to Compress Gradients in Federated Fune-tuning for Large Language Models—0
A Systematic Literature Review on Federated Learning: From A Model Quality Perspective—0
A Systematic Literature Review on Federated Machine Learning: From A Software Engineering Perspective—0
A Generalized Meta Federated Learning Framework with Theoretical Convergence Guarantees—0
Evaluating Multi-Global Server Architecture for Federated Learning—0
Adaptive Control of Client Selection and Gradient Compression for Efficient Federated Learning—0
Evaluating the Communication Efficiency in Federated Learning Algorithms—0
Evaluating the Potential of Federated Learning for Maize Leaf Disease Prediction—0
Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning—0
Enhancing Convergence in Federated Learning: A Contribution-Aware Asynchronous Approach—0
Fairness in Federated Learning for Spatial-Temporal Applications—0
Fake It Till Make It: Federated Learning with Consensus-Oriented Generation—0
Evaluation of Hyperparameter-Optimization Approaches in an Industrial Federated Learning System—0
Event-Driven Online Vertical Federated Learning—0
Event-Triggered Decentralized Federated Learning over Resource-Constrained Edge Devices—0
Evidential Federated Learning for Skin Lesion Image Classification—0
EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning—0
Fast-Convergent and Communication-Alleviated Heterogeneous Hierarchical Federated Learning in Autonomous Driving—0
Cross-Silo Federated Learning for Multi-Tier Networks with Vertical and Horizontal Data Partitioning—0
Cross-Silo Federated Learning: Challenges and Opportunities—0
Exact Support Recovery in Federated Regression with One-shot Communication—0
A Systematic Literature Review on Client Selection in Federated Learning—0
ExclaveFL: Providing Transparency to Federated Learning using Exclaves—0
Expanding the Reach of Federated Learning by Reducing Client Resource Requirements—0
Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression—0
A Systematic Literature Review on Blockchain Enabled Federated Learning Framework for Internet of Vehicles—0
Adaptive Coded Federated Learning: Privacy Preservation and Straggler Mitigation—0
Cross-Modal Vertical Federated Learning for MRI Reconstruction—0
Cross-Modal Prototype based Multimodal Federated Learning under Severely Missing Modality—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