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 43014350 of 6771 papers

TitleStatusHype
Personalized Federated Learning with Communication Compression0
Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning using Independent Component Analysis0
Towards More Efficient Data Valuation in Healthcare Federated Learning using Ensembling0
Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillation0
Anomaly Detection through Unsupervised Federated LearningCode0
FedDAR: Federated Domain-Aware Representation Learning0
FADE: Enabling Federated Adversarial Training on Heterogeneous Resource-Constrained Edge Devices0
A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated LearningCode0
Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks0
Modular Federated Learning0
Cerberus: Exploring Federated Prediction of Security Events0
Faster federated optimization under second-order similarity0
Boost Decentralized Federated Learning in Vehicular Networks by Diversifying Data Sources0
Federated Transfer Learning with Multimodal Data0
Federated Zero-Shot Learning for Visual Recognition0
Suppressing Noise from Built Environment Datasets to Reduce Communication Rounds for Convergence of Federated Learning0
Federated XGBoost on Sample-Wise Non-IID Data0
FedAR+: A Federated Learning Approach to Appliance Recognition with Mislabeled Data in Residential Buildings0
Predictive GAN-powered Multi-Objective Optimization for Hybrid Federated Split Learning0
Non-IID Quantum Federated Learning with One-shot Communication ComplexityCode1
Trading Off Privacy, Utility and Efficiency in Federated Learning0
To Store or Not? Online Data Selection for Federated Learning with Limited Storage0
Federated Learning with Label Distribution Skew via Logits CalibrationCode1
Versatile Single-Loop Method for Gradient Estimator: First and Second Order Optimality, and its Application to Federated Learning0
Reducing Impacts of System Heterogeneity in Federated Learning using Weight Update Magnitudes0
Online Meta-Learning for Model Update Aggregation in Federated Learning for Click-Through Rate Prediction0
FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphsCode1
Exploring Semantic Attributes from A Foundation Model for Federated Learning of Disjoint Label Spaces0
Effectiveness of Federated Learning and CNN Ensemble Architectures for Identifying Brain Tumors Using MRI ImagesCode0
Federated Learning of Large Models at the Edge via Principal Sub-Model TrainingCode0
A Federated Learning-enabled Smart Street Light Monitoring Application: Benefits and Future Challenges0
BOBA: Byzantine-Robust Federated Learning with Label SkewnessCode0
Tensor Decomposition based Personalized Federated Learning0
Lottery Aware Sparsity Hunting: Enabling Federated Learning on Resource-Limited EdgeCode0
Network-Level Adversaries in Federated LearningCode0
Abnormal Local Clustering in Federated Learning0
Federated and Privacy-Preserving Learning of Accounting Data in Financial Statement Audits0
Towards Federated Learning against Noisy Labels via Local Self-RegularizationCode1
On Differential Privacy for Federated Learning in Wireless Systems with Multiple Base Stations0
FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learning0
DPAUC: Differentially Private AUC Computation in Federated LearningCode2
PromptFL: Let Federated Participants Cooperatively Learn Prompts Instead of Models -- Federated Learning in Age of Foundation Model0
Federated Self-Supervised Contrastive Learning and Masked Autoencoder for Dermatological Disease Diagnosis0
Towards Sparsified Federated Neuroimaging Models via Weight Pruning0
Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge EnvironmentsCode1
Achieving Fairness in Dermatological Disease Diagnosis through Automatic Weight Adjusting Federated Learning and Personalization0
Exact Penalty Method for Federated LearningCode0
FedMCSA: Personalized Federated Learning via Model Components Self-Attention0
Towards Communication Efficient and Fair Federated Personalized Sequential Recommendation0
Application of federated learning techniques for arrhythmia classification using 12-lead ECG signals0
Show:102550
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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