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 22012250 of 6771 papers

TitleStatusHype
Contextual Stochastic Bilevel Optimization0
Adapting MLOps for Diverse In-Network Intelligence in 6G Era: Challenges and Solutions0
1-Bit Compressive Sensing for Efficient Federated Learning Over the Air0
FedDD: Toward Communication-efficient Federated Learning with Differential Parameter Dropout0
Contextual Model Aggregation for Fast and Robust Federated Learning in Edge Computing0
Contextual Combinatorial Multi-output GP Bandits with Group Constraints0
A Survey on Vertical Federated Learning: From a Layered Perspective0
Context-Aware Online Client Selection for Hierarchical Federated Learning0
Content Popularity Prediction in Fog-RANs: A Clustered Federated Learning Based Approach0
A Survey on the Role of Artificial Intelligence and Machine Learning in 6G-V2X Applications0
Affect-driven Ordinal Engagement Measurement from Video0
Content Popularity Prediction Based on Quantized Federated Bayesian Learning in Fog Radio Access Networks0
A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computing0
AdapterFL: Adaptive Heterogeneous Federated Learning for Resource-constrained Mobile Computing Systems0
A Survey on Point-of-Interest Recommendation: Models, Architectures, and Security0
Constrained Differentially Private Federated Learning for Low-bandwidth Devices0
A few-shot Label Unlearning in Vertical Federated Learning0
FedDCL: a federated data collaboration learning as a hybrid-type privacy-preserving framework based on federated learning and data collaboration0
Considerations on the Theory of Training Models with Differential Privacy0
Connecting Federated ADMM to Bayes0
A Survey on Participant Selection for Federated Learning in Mobile Networks0
A Survey on Model-based, Heuristic, and Machine Learning Optimization Approaches in RIS-aided Wireless Networks0
Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information0
A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs0
Conformal Prediction for Federated Uncertainty Quantification Under Label Shift0
Conformalized Prediction of Post-Fault Voltage Trajectories Using Pre-trained and Finetuned Attention-Driven Neural Operators0
A Survey on Heterogeneous Federated Learning0
Confined Gradient Descent: Privacy-preserving Optimization for Federated Learning0
Adapter-based Selective Knowledge Distillation for Federated Multi-domain Meeting Summarization0
HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems0
FedDA-TSformer: Federated Domain Adaptation with Vision TimeSformer for Left Ventricle Segmentation on Gated Myocardial Perfusion SPECT Image0
FedDec: Peer-to-peer Aided Federated Learning0
Confidence-based federated distillation for vision-based lane-centering0
A Survey on Federated Recommendation Systems0
Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligence0
Confederated Learning: Federated Learning with Decentralized Edge Servers0
A Survey on Federated Learning in Human Sensing0
A Federated Parameter Aggregation Method for Node Classification Tasks with Different Graph Network Structures0
ConDa: Fast Federated Unlearning with Contribution Dampening0
A Survey on Federated Learning for the Healthcare Metaverse: Concepts, Applications, Challenges, and Future Directions0
Concurrent vertical and horizontal federated learning with fuzzy cognitive maps0
Concept Matching: Clustering-based Federated Continual Learning0
A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things0
A Federated Online Restless Bandit Framework for Cooperative Resource Allocation0
Adap DP-FL: Differentially Private Federated Learning with Adaptive Noise0
Concept Drift Detection in Federated Networked Systems0
Concept drift detection and adaptation for federated and continual learning0
Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning0
Compute-Update Federated Learning: A Lattice Coding Approach Over-the-Air0
A Survey on Efficient Federated Learning Methods for Foundation Model Training0
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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