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 65516600 of 6771 papers

TitleStatusHype
Enhancing the Convergence of Federated Learning Aggregation Strategies with Limited Data0
Enhancing the Performance of Global Model by Improving the Adaptability of Local Models in Federated Learning0
Enhancing the Privacy of Federated Learning with Sketching0
Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning0
Enhancing Vehicle Environmental Awareness via Federated Learning and Automatic Labeling0
Enhancing Visual Representation with Textual Semantics: Textual Semantics-Powered Prototypes for Heterogeneous Federated Learning0
Ensemble Attention Distillation for Privacy-Preserving Federated Learning0
Ensemble Federated Adversarial Training with Non-IID data0
Ensemble Federated Learning: an approach for collaborative pneumonia diagnosis0
Entity Augmentation for Efficient Classification of Vertically Partitioned Data with Limited Overlap0
Entity Resolution and Federated Learning get a Federated Resolution0
EP-GAN: Unsupervised Federated Learning with Expectation-Propagation Prior GAN0
EPIC: Enhancing Privacy through Iterative Collaboration0
Epidemic Decision-making System Based Federated Reinforcement Learning0
Equitable Federated Learning with Activation Clustering0
Equitable-FL: Federated Learning with Sparsity for Resource-Constrained Environment0
Escaping Data Scarcity for High-Resolution Heterogeneous Face Hallucination0
Escaping Saddle Points in Distributed Newton's Method with Communication Efficiency and Byzantine Resilience0
Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression0
Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning0
ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devices0
ESMFL: Efficient and Secure Models for Federated Learning0
Estimation of Individual Device Contributions for Incentivizing Federated Learning0
E-Tree Learning: A Novel Decentralized Model Learning Framework for Edge AI0
Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning0
Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challenges0
Evaluating Multi-Global Server Architecture for Federated Learning0
Evaluating Query Efficiency and Accuracy of Transfer Learning-based Model Extraction Attack in Federated Learning0
Evaluating the Communication Efficiency in Federated Learning Algorithms0
Evaluating the Potential of Federated Learning for Maize Leaf Disease Prediction0
Evaluation and comparison of federated learning algorithms for Human Activity Recognition on smartphones0
Evaluation of Hyperparameter-Optimization Approaches in an Industrial Federated Learning System0
Event-Driven Online Vertical Federated Learning0
Event-Triggered Decentralized Federated Learning over Resource-Constrained Edge Devices0
Evidential Federated Learning for Skin Lesion Image Classification0
EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning0
Exact Support Recovery in Federated Regression with One-shot Communication0
Examining Modality Incongruity in Multimodal Federated Learning for Medical Vision and Language-based Disease Detection0
ExclaveFL: Providing Transparency to Federated Learning using Exclaves0
Expanding the Reach of Federated Learning by Reducing Client Resource Requirements0
Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression0
Experimental Demonstration of Over the Air Federated Learning for Cellular Networks0
Experiments of Federated Learning for COVID-19 Chest X-ray Images0
Explainability and Continual Learning meet Federated Learning at the Network Edge0
Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine0
Explainable Machine Learning-Based Security and Privacy Protection Framework for Internet of Medical Things Systems0
Explainable Semantic Federated Learning Enabled Industrial Edge Network for Fire Surveillance0
Explanation-Guided Fair Federated Learning for Transparent 6G RAN Slicing0
Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning0
Exploiting Features and Logits in Heterogeneous Federated Learning0
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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