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 801850 of 6771 papers

TitleStatusHype
Overcoming Data and Model Heterogeneities in Decentralized Federated Learning via Synthetic AnchorsCode1
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine LearningCode1
Over-the-Air Federated Learning via Second-Order OptimizationCode1
Parameterized Knowledge Transfer for Personalized Federated LearningCode1
Passive Inference Attacks on Split Learning via Adversarial RegularizationCode1
Benchmarking Algorithms for Federated Domain GeneralizationCode1
Performance Optimization for Federated Person Re-identification via Benchmark AnalysisCode1
Personalized Federated Continual Learning via Multi-granularity PromptCode1
Benchmarking Differential Privacy and Federated Learning for BERT ModelsCode1
Personalized Federated Learning by Structured and Unstructured Pruning under Data HeterogeneityCode1
Personalized Federated Learning using HypernetworksCode1
Personalized Federated Learning via Variational Bayesian InferenceCode1
Personalized Federated Learning With GraphCode1
Personalized Federated Learning with Feature Alignment and Classifier CollaborationCode1
Federated Learning via Posterior Averaging: A New Perspective and Practical AlgorithmsCode1
Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order OptimizationCode1
Personalizing Federated Medical Image Segmentation via Local CalibrationCode1
PFA: Privacy-preserving Federated Adaptation for Effective Model PersonalizationCode1
PFL-MoE: Personalized Federated Learning Based on Mixture of ExpertsCode1
Make Landscape Flatter in Differentially Private Federated LearningCode1
A Novel Privacy-Preserved Recommender System Framework based on Federated Learning0
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
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
Can Fair Federated Learning reduce the need for Personalisation?0
A Novel Neural Network-Based Federated Learning System for Imbalanced and Non-IID Data0
A Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoT0
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
A Novel Attribute Reconstruction Attack in Federated Learning0
A Novel Algorithm for Personalized Federated Learning: Knowledge Distillation with Weighted Combination Loss0
An Optimization Framework for Federated Edge Learning0
An Optimal Transport Approach to Personalized Federated Learning0
Advances and Open Challenges in Federated Foundation Models0
A Comparative Study of Sampling Methods with Cross-Validation in the FedHome Framework0
An Operator Splitting View of Federated Learning0
Anonymizing Data for Privacy-Preserving Federated Learning0
Advances and Challenges in Meta-Learning: A Technical Review0
An On-Device Federated Learning Approach for Cooperative Model Update between Edge Devices0
Advancements of federated learning towards privacy preservation: from federated learning to split learning0
A Comparative Study of Federated Learning Models for COVID-19 Detection0
Accelerated Convergence of Stochastic Heavy Ball Method under Anisotropic Gradient Noise0
Random Client Selection on Contrastive Federated Learning for Tabular Data0
Anomaly Detection via Federated Learning0
Advancements in Federated Learning: Models, Methods, and Privacy0
Advanced Relay-Based Collaborative Framework for Optimizing Synchronization in Split Federated Learning over Wireless Networks0
Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach0
A Comparative Evaluation of FedAvg and Per-FedAvg Algorithms for Dirichlet Distributed Heterogeneous Data0
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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