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 31513200 of 6771 papers

TitleStatusHype
Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions0
Federated Transfer-Ordered-Personalized Learning for Driver Monitoring Application0
DABS: Data-Agnostic Backdoor attack at the Server in Federated Learning0
FederatedTrust: A Solution for Trustworthy Federated Learning0
Federated Learning for Localization: A Privacy-Preserving Crowdsourcing Method0
D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data0
Federated Two-stage Learning with Sign-based Voting0
Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous Agents0
Federated Unbiased Learning to Rank0
Federated Uncertainty-Aware Aggregation for Fundus Diabetic Retinopathy Staging0
ATPFL: Automatic Trajectory Prediction Model Design Under Federated Learning Framework0
Federated Unlearning0
Federated Learning for Large-Scale Scene Modeling with Neural Radiance Fields0
Federated Unlearning for Human Activity Recognition0
D2p-fed:Differentially Private Federated Learning with Efficient Communication0
Federated Learning for IoUT: Concepts, Applications, Challenges and Opportunities0
Federated Learning for Intrusion Detection in IoT Security: A Hybrid Ensemble Approach0
Federated Unlearning via Active Forgetting0
CyclicFL: A Cyclic Model Pre-Training Approach to Efficient Federated Learning0
ATM: Improving Model Merging by Alternating Tuning and Merging0
Federated Unlearning with Knowledge Distillation0
Federated Unsupervised Domain Adaptation for Face Recognition0
A Generative Framework for Personalized Learning and Estimation: Theory, Algorithms, and Privacy0
Federated Unsupervised Representation Learning0
Federated Unsupervised Semantic Segmentation0
Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions0
Federated User Representation Learning0
Federated Variational Inference for Bayesian Mixture Models0
Cyclical Weight Consolidation: Towards Solving Catastrophic Forgetting in Serial Federated Learning0
Federated Variational Inference: Towards Improved Personalization and Generalization0
Federated Virtual Learning on Heterogeneous Data with Local-global Distillation0
Federated Learning for Internet of Things: A Comprehensive Survey0
Federated Learning for Internet of Things: Applications, Challenges, and Opportunities0
Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures0
CYCle: Choosing Your Collaborators Wisely to Enhance Collaborative Fairness in Decentralized Learning0
Federated XGBoost on Sample-Wise Non-IID Data0
A Thorough Assessment of the Non-IID Data Impact in Federated Learning0
Exploring Semantic Attributes from A Foundation Model for Federated Learning of Disjoint Label Spaces0
Federated Learning for Inference at Anytime and Anywhere0
Federated Learning for Industrial Internet of Things in Future Industries0
Cybersecurity Threats in Connected and Automated Vehicles based Federated Learning Systems0
FedET: A Communication-Efficient Federated Class-Incremental Learning Framework Based on Enhanced Transformer0
FeDETR: a Federated Approach for Stenosis Detection in Coronary Angiography0
FedEval: A Holistic Evaluation Framework for Federated Learning0
Federated Learning for ICD Classification with Lightweight Models and Pretrained Embeddings0
Federated Learning for Healthcare Informatics0
A Theoretical Analysis of Efficiency Constrained Utility-Privacy Bi-Objective Optimization in Federated Learning0
SGDE: Secure Generative Data Exchange for Cross-Silo Federated Learning0
Adaptive Deadline and Batch Layered Synchronized Federated Learning0
Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges0
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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