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 451500 of 6771 papers

TitleStatusHype
Federated Learning under Distributed Concept DriftCode1
FedFormer: Contextual Federation with Attention in Reinforcement LearningCode1
Can Foundation Models Help Us Achieve Perfect Secrecy?Code1
FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias ReductionCode1
Masked Jigsaw Puzzle: A Versatile Position Embedding for Vision TransformersCode1
Towards a Defense Against Federated Backdoor Attacks Under Continuous TrainingCode1
Optimizing Performance of Federated Person Re-identification: Benchmarking and AnalysisCode1
Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy GuaranteesCode1
Orchestra: Unsupervised Federated Learning via Globally Consistent ClusteringCode1
Test-Time Robust Personalization for Federated LearningCode1
FedAdapter: Efficient Federated Learning for Modern NLPCode1
FedILC: Weighted Geometric Mean and Invariant Gradient Covariance for Federated Learning on Non-IID DataCode1
Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical ImagingCode1
Recovering Private Text in Federated Learning of Language ModelsCode1
Federated Learning Under Intermittent Client Availability and Time-Varying Communication ConstraintsCode1
ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated LearningCode1
Communication-Efficient Adaptive Federated LearningCode1
FedMix: Mixed Supervised Federated Learning for Medical Image SegmentationCode1
FedGiA: An Efficient Hybrid Algorithm for Federated LearningCode1
FEDIC: Federated Learning on Non-IID and Long-Tailed Data via Calibrated DistillationCode1
Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated FeaturesCode1
Poisoning Deep Learning Based Recommender Model in Federated Learning ScenariosCode1
Federated Learning Enables Big Data for Rare Cancer Boundary DetectionCode1
Federated Learning via Inexact ADMMCode1
FedKL: Tackling Data Heterogeneity in Federated Reinforcement Learning by Penalizing KL DivergenceCode1
IOP-FL: Inside-Outside Personalization for Federated Medical Image SegmentationCode1
Exploring the Distributed Knowledge Congruence in Proxy-data-free Federated DistillationCode1
FedCorr: Multi-Stage Federated Learning for Label Noise CorrectionCode1
Federated Learning with Partial Model PersonalizationCode1
Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing ClientsCode1
Federated Cross Learning for Medical Image SegmentationCode1
FedRecAttack: Model Poisoning Attack to Federated RecommendationCode1
Federated Learning Framework Coping with Hierarchical Heterogeneity in Cooperative ITSCode1
Over-the-Air Federated Learning via Second-Order OptimizationCode1
Auditing Privacy Defenses in Federated Learning via Generative Gradient LeakageCode1
RSCFed: Random Sampling Consensus Federated Semi-supervised LearningCode1
FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning SimulationsCode1
Federated Class-Incremental LearningCode1
FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and CorrectionCode1
Improving Generalization in Federated Learning by Seeking Flat MinimaCode1
Efficient Split-Mix Federated Learning for On-Demand and In-Situ CustomizationCode1
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding AggregationCode1
Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated LearningCode1
SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge IntelligenceCode1
Privacy-Aware Compression for Federated Data AnalysisCode1
Semi-FedSER: Semi-supervised Learning for Speech Emotion Recognition On Federated Learning using Multiview Pseudo-LabelingCode1
Energy-Latency Attacks via Sponge PoisoningCode1
CoCoFL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and QuantizationCode1
Acceleration of Federated Learning with Alleviated Forgetting in Local TrainingCode1
Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed DataCode1
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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