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 3751–3800 of 6771 papers

TitleStatusHype
Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detection—0
StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systems—0
State-of-the-art Techniques in Deep Edge Intelligence—0
Statistical Estimation and Inference via Local SGD in Federated Learning—0
StatMix: Data augmentation method that relies on image statistics in federated learning—0
STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning—0
ST-FL: Style Transfer Preprocessing in Federated Learning for COVID-19 Segmentation—0
STHFL: Spatio-Temporal Heterogeneous Federated Learning—0
Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learning—0
StochaLM: a Stochastic alternate Linearization Method for distributed optimization—0
Stochastic Approximation Approach to Federated Machine Learning—0
Stochastic Channel-Based Federated Learning for Medical Data Privacy Preserving—0
Stochastic Client Selection for Federated Learning with Volatile Clients—0
Stochastic Clustered Federated Learning—0
Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Design—0
Stochastic Coded Federated Learning with Convergence and Privacy Guarantees—0
Stochastic Communication Avoidance for Recommendation Systems—0
Stochastic Controlled Averaging for Federated Learning with Communication Compression—0
Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees—0
Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization—0
Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity—0
Straggler-resilient Federated Learning: Tackling Computation Heterogeneity with Layer-wise Partial Model Training in Mobile Edge Network—0
Stragglers Are Not Disaster: A Hybrid Federated Learning Algorithm with Delayed Gradients—0
Strategic Client Selection to Address Non-IIDness in HAPS-enabled FL Networks—0
Stratified cross-validation for unbiased and privacy-preserving federated learning—0
Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Sampling—0
Streaming Federated Learning with Markovian Data—0
Streamlined Federated Unlearning: Unite as One to Be Highly Efficient—0
Structured Reinforcement Learning for Incentivized Stochastic Covert Optimization—0
Studying the Robustness of Anti-adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors—0
Study of the performance and scalability of federated learning for medical imaging with intermittent clients—0
Subject Data Auditing via Source Inference Attack in Cross-Silo Federated Learning—0
Subject Granular Differential Privacy in Federated Learning—0
Subject Membership Inference Attacks in Federated Learning—0
Sublinear Algorithms for Wasserstein and Total Variation Distances: Applications to Fairness and Privacy Auditing—0
Federated Learning over Hierarchical Wireless Networks: Training Latency Minimization via Submodel Partitioning—0
Submodular Maximization Approaches for Equitable Client Selection in Federated Learning—0
Subspace based Federated Unlearning—0
Subspace Recovery from Heterogeneous Data with Non-isotropic Noise—0
Substra: a framework for privacy-preserving, traceable and collaborative Machine Learning—0
Subword Embedding from Bytes Gains Privacy without Sacrificing Accuracy and Complexity—0
Summary of Point Transformer with Federated Learning for Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained Whole Slide Images—0
SuperFedNAS: Cost-Efficient Federated Neural Architecture Search for On-Device Inference—0
Supersonic OT: Fast Unconditionally Secure Oblivious Transfer—0
Support Vector Based Anomaly Detection in Federated Learning—0
Suppressing Noise from Built Environment Datasets to Reduce Communication Rounds for Convergence of Federated Learning—0
Survey of Federated Learning Models for Spatial-Temporal Mobility Applications—0
Survey of Personalization Techniques for Federated Learning—0
Survey of Privacy Threats and Countermeasures in Federated Learning—0
Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks—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