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 15511600 of 6771 papers

TitleStatusHype
Federated Causal Discovery From InterventionsCode0
FedCert: Federated Accuracy CertificationCode0
Communication-Efficient ADMM-based Federated LearningCode0
FedCCRL: Federated Domain Generalization with Cross-Client Representation LearningCode0
FedBChain: A Blockchain-enabled Federated Learning Framework for Improving DeepConvLSTM with Comparative Strategy InsightsCode0
MARINA: Faster Non-Convex Distributed Learning with CompressionCode0
Masked Autoencoders are Parameter-Efficient Federated Continual LearnersCode0
Maximum Knowledge Orthogonality Reconstruction with Gradients in Federated LearningCode0
FedBug: A Bottom-Up Gradual Unfreezing Framework for Federated LearningCode0
FedBM: Stealing Knowledge from Pre-trained Language Models for Heterogeneous Federated LearningCode0
FedBrain-Distill: Communication-Efficient Federated Brain Tumor Classification Using Ensemble Knowledge Distillation on Non-IID DataCode0
Memory-aware curriculum federated learning for breast cancer classificationCode0
FedCAP: Robust Federated Learning via Customized Aggregation and PersonalizationCode0
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved RatesCode0
MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central UpdatesCode0
FedBKD: Distilled Federated Learning to Embrace Gerneralization and Personalization on Non-IID DataCode0
FedCAR: Cross-client Adaptive Re-weighting for Generative Models in Federated LearningCode0
FedAli: Personalized Federated Learning with Aligned Prototypes through Optimal TransportCode0
FedAPM: Federated Learning via ADMM with Partial Model PersonalizationCode0
FedAH: Aggregated Head for Personalized Federated LearningCode0
Feature Reconstruction Attacks and Countermeasures of DNN training in Vertical Federated LearningCode0
Feature Norm Regularized Federated Learning: Transforming Skewed Distributions into Global InsightsCode0
FBFL: A Field-Based Coordination Approach for Data Heterogeneity in Federated LearningCode0
FCA: Taming Long-tailed Federated Medical Image Classification by Classifier AnchoringCode0
FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial LearningCode0
Federated Learning with Convex Global and Local ConstraintsCode0
CollaFuse: Navigating Limited Resources and Privacy in Collaborative Generative AICode0
Fairness and Privacy in Federated Learning and Their Implications in HealthcareCode0
FDAPT: Federated Domain-adaptive Pre-training for Language ModelsCode0
Differentially Private Decentralized Learning with Random WalksCode0
Motley: Benchmarking Heterogeneity and Personalization in Federated LearningCode0
CollaFuse: Collaborative Diffusion ModelsCode0
FAIR-FATE: Fair Federated Learning with MomentumCode0
F3: Fair and Federated Face Attribute Classification with Heterogeneous DataCode0
Reducing Training Time in Cross-Silo Federated Learning using Multigraph TopologyCode0
Collaborative Unsupervised Visual Representation Learning from Decentralized DataCode0
Collaborative Training of Medical Artificial Intelligence Models with non-uniform LabelsCode0
FACT: Federated Adversarial Cross TrainingCode0
FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?Code0
Multimodal Federated Learning with Missing Modality via Prototype Mask and ContrastCode0
FAA-CLIP: Federated Adversarial Adaptation of CLIPCode0
Aergia: Leveraging Heterogeneity in Federated Learning SystemsCode0
FADAS: Towards Federated Adaptive Asynchronous OptimizationCode0
Exploring Selective Layer Fine-Tuning in Federated LearningCode0
Bayesian Nonparametric Federated Learning of Neural NetworksCode0
Near-Optimal Collaborative Learning in BanditsCode0
Differentially Private Federated Learning via Reconfigurable Intelligent SurfaceCode0
Differentially-Private Federated Linear BanditsCode0
Exploiting Unintended Feature Leakage in Collaborative LearningCode0
Exploring Data Redundancy in Real-world Image Classification through Data SelectionCode0
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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