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 401450 of 6771 papers

TitleStatusHype
Non-IID Quantum Federated Learning with One-shot Communication ComplexityCode1
Federated Learning with Label Distribution Skew via Logits CalibrationCode1
FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphsCode1
Towards Federated Learning against Noisy Labels via Local Self-RegularizationCode1
Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge EnvironmentsCode1
Joint Privacy Enhancement and Quantization in Federated LearningCode1
FLIS: Clustered Federated Learning via Inference Similarity for Non-IID Data DistributionCode1
A Hybrid Self-Supervised Learning Framework for Vertical Federated LearningCode1
An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public TransportationCode1
Practical Vertical Federated Learning with Unsupervised Representation LearningCode1
FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated LearningCode1
Asynchronous Federated Learning for Edge-assisted Vehicular NetworksCode1
Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement LearningCode1
Differentially Private Vertical Federated ClusteringCode1
Reconciling Security and Communication Efficiency in Federated LearningCode1
Spatial-Temporal Federated Learning for Lifelong Person Re-identification on Distributed EdgesCode1
UniFed: All-In-One Federated Learning Platform to Unify Open-Source FrameworksCode1
FedX: Unsupervised Federated Learning with Cross Knowledge DistillationCode1
FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious ClientsCode1
FLAIR: Federated Learning Annotated Image RepositoryCode1
Communication-Efficient Diffusion Strategy for Performance Improvement of Federated Learning with Non-IID DataCode1
Multi-Level Branched Regularization for Federated LearningCode1
One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI SynthesisCode1
Federated Unlearning: How to Efficiently Erase a Client in FL?Code1
Personalizing Federated Medical Image Segmentation via Local CalibrationCode1
Towards the Practical Utility of Federated Learning in the Medical DomainCode1
Federated Self-supervised Learning for Video UnderstandingCode1
Where to Begin? On the Impact of Pre-Training and Initialization in Federated LearningCode1
FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image ClassificationCode1
An Empirical Study of Personalized Federated LearningCode1
Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class ImbalanceCode1
How to Combine Variational Bayesian Networks in Federated LearningCode1
Personalized Subgraph Federated LearningCode1
Mitigating Data Heterogeneity in Federated Learning with Data AugmentationCode1
Shuffle Gaussian Mechanism for Differential PrivacyCode1
Pisces: Efficient Federated Learning via Guided Asynchronous TrainingCode1
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated LearningCode1
FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image ClassificationCode1
On Privacy and Personalization in Cross-Silo Federated LearningCode1
Personalized Federated Learning via Variational Bayesian InferenceCode1
Federated Multi-organ Segmentation with Inconsistent LabelsCode1
Federated Optimization Algorithms with Random Reshuffling and Gradient CompressionCode1
Neurotoxin: Durable Backdoors in Federated LearningCode1
Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT ImagingCode1
Privacy Amplification via Shuffled Check-InsCode1
Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated LearningCode1
Pretrained Models for Multilingual Federated LearningCode1
Federated Deep Learning Meets Autonomous Vehicle Perception: Design and VerificationCode1
A federated graph neural network framework for privacy-preserving personalizationCode1
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse TrainingCode1
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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