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 17511800 of 6771 papers

TitleStatusHype
Client-specific Property Inference against Secure Aggregation in Federated LearningCode0
Enable the Right to be Forgotten with Federated Client Unlearning in Medical ImagingCode0
Encrypted machine learning of molecular quantum propertiesCode0
End-to-End Verifiable Decentralized Federated LearningCode0
SemiSFL: Split Federated Learning on Unlabeled and Non-IID DataCode0
Efficient Vertical Federated Learning Method for Ridge Regression of Large-Scale Samples via Least-Squares SolutionCode0
Embedding-Based Federated Data Sharing via Differentially Private Conditional VAEsCode0
Client Selection for Federated Learning with Heterogeneous Resources in Mobile EdgeCode0
Embedding Byzantine Fault Tolerance into Federated Learning via Virtual Data-Driven Consistency Scoring PluginCode0
Efficient and Robust Regularized Federated RecommendationCode0
Efficient Federated Intrusion Detection in 5G ecosystem using optimized BERT-based modelCode0
Client Recruitment for Federated Learning in ICU Length of Stay PredictionCode0
Efficient Federated Learning against Heterogeneous and Non-stationary Client UnavailabilityCode0
Effective Heterogeneous Federated Learning via Efficient Hypernetwork-based Weight GenerationCode0
Dynamically Weighted Federated k-MeansCode0
BOBA: Byzantine-Robust Federated Learning with Label SkewnessCode0
Effectiveness of Federated Learning and CNN Ensemble Architectures for Identifying Brain Tumors Using MRI ImagesCode0
Client-Edge-Cloud Hierarchical Federated LearningCode0
Empowering Data Mesh with Federated LearningCode0
FedAH: Aggregated Head for Personalized Federated LearningCode0
A Novel Privacy-Preserved Recommender System Framework based on Federated Learning0
Citadel: Protecting Data Privacy and Model Confidentiality for Collaborative Learning with SGX0
A Novel Pearson Correlation-Based Merging Algorithm for Robust Distributed Machine Learning with Heterogeneous Data0
Advancing Deep Learning through Probability Engineering: A Pragmatic Paradigm for Modern AI0
Chu-ko-nu: A Reliable, Efficient, and Anonymously Authentication-Enabled Realization for Multi-Round Secure Aggregation in Federated Learning0
Chisme: Fully Decentralized Differentiated Deep Learning for Edge Intelligence0
Characterization of the Global Bias Problem in Aerial Federated Learning0
Channel-driven Decentralized Bayesian Federated Learning for Trustworthy Decision Making in D2D Networks0
A novel parameter decoupling approach of personalized federated learning for image analysis0
A Comprehensive Review of Techniques, Algorithms, Advancements, Challenges, and Clinical Applications of Multi-modal Medical Image Fusion for Improved Diagnosis0
Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning0
A Novel Neural Network-Based Federated Learning System for Imbalanced and Non-IID Data0
Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Images0
Challenges and Approaches for Mitigating Byzantine Attacks in Federated Learning0
A Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoT0
CG-FedLLM: How to Compress Gradients in Federated Fune-tuning for Large Language Models0
CFLIT: Coexisting Federated Learning and Information Transfer0
CFedAvg: Achieving Efficient Communication and Fast Convergence in Non-IID Federated Learning0
A Novel Federated Learning-Based IDS for Enhancing UAVs Privacy and Security0
Advances in Robust Federated Learning: Heterogeneity Considerations0
Accelerated Federated Learning with Decoupled Adaptive Optimization0
Certified Robustness for Free in Differentially Private Federated Learning0
Certified Federated Adversarial Training0
A Novel Attribute Reconstruction Attack in Federated Learning0
Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing0
Cerberus: Exploring Federated Prediction of Security Events0
A Novel Algorithm for Personalized Federated Learning: Knowledge Distillation with Weighted Combination Loss0
Centroid Approximation for Byzantine-Tolerant Federated Learning0
An Optimization Framework for Federated Edge Learning0
An Optimal Transport Approach to Personalized Federated Learning0
Show:102550
← PrevPage 36 of 136Next →

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