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 651700 of 6771 papers

TitleStatusHype
Federated Learning via Inexact ADMMCode1
Federated Learning via Input-Output Collaborative DistillationCode1
Personalized Federated Learning with Adaptive Batchnorm for HealthcareCode1
Federated Learning with Bilateral Curation for Partially Class-Disjoint DataCode1
Federated Learning with Superquantile Aggregation for Heterogeneous DataCode1
Federated Learning with Label Distribution Skew via Logits CalibrationCode1
Federated Learning with Matched AveragingCode1
Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated Graph Neural NetworkCode1
Federated Learning with Quantum Computing and Fully Homomorphic Encryption: A Novel Computing Paradigm Shift in Privacy-Preserving MLCode1
FedDR -- Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite OptimizationCode1
Federated Learning with Taskonomy for Non-IID DataCode1
Federated Modality-specific Encoders and Multimodal Anchors for Personalized Brain Tumor SegmentationCode1
Ditto: Fair and Robust Federated Learning Through PersonalizationCode1
Federated Multi-Task Learning under a Mixture of DistributionsCode1
Federated Neuromorphic Learning of Spiking Neural Networks for Low-Power Edge IntelligenceCode1
Federated nnU-Net for Privacy-Preserving Medical Image SegmentationCode1
Federated Recommendation with Additive PersonalizationCode1
An In-Depth Evaluation of Federated Learning on Biomedical Natural Language ProcessingCode1
Omnidirectional Transfer for Quasilinear Lifelong LearningCode1
Federated Robustness Propagation: Sharing Robustness in Heterogeneous Federated LearningCode1
Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT ImagingCode1
Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked DataCode1
Federated Unlearning: How to Efficiently Erase a Client in FL?Code1
Federated Unlearning with Gradient Descent and Conflict MitigationCode1
FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous DataCode1
FedFed: Feature Distillation against Data Heterogeneity in Federated LearningCode1
FedFly: Towards Migration in Edge-based Distributed Federated LearningCode1
Beyond Traditional Threats: A Persistent Backdoor Attack on Federated LearningCode1
FedFusion: Manifold Driven Federated Learning for Multi-satellite and Multi-modality FusionCode1
A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data SourcesCode1
Bias Propagation in Federated LearningCode1
Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive CollaborationCode1
Attack of the Tails: Yes, You Really Can Backdoor Federated LearningCode1
FedGroup: Efficient Clustered Federated Learning via Decomposed Data-Driven MeasureCode1
FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal DecouplingCode1
FEDIC: Federated Learning on Non-IID and Long-Tailed Data via Calibrated DistillationCode1
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated LearningCode1
Attribute Inference Attack of Speech Emotion Recognition in Federated Learning SettingsCode1
FedKL: Tackling Data Heterogeneity in Federated Reinforcement Learning by Penalizing KL DivergenceCode1
FedLab: A Flexible Federated Learning FrameworkCode1
FedLess: Secure and Scalable Federated Learning Using Serverless ComputingCode1
Benchmarking Differential Privacy and Federated Learning for BERT ModelsCode1
FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-supervised Medical Image SegmentationCode1
FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter SharingCode1
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of ModelsCode1
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine LearningCode1
Auditing Privacy Defenses in Federated Learning via Generative Gradient LeakageCode1
FedMedICL: Towards Holistic Evaluation of Distribution Shifts in Federated Medical ImagingCode1
Decentralized Federated Learning: A Segmented Gossip ApproachCode1
Benchmarking Algorithms for Federated Domain GeneralizationCode1
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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