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 14011450 of 6771 papers

TitleStatusHype
Data Subsampling for Bayesian Neural NetworksCode0
Federated Continual Learning for Text Classification via Selective Inter-client TransferCode0
Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication ComplexityCode0
Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning ApplicationsCode0
Communication-Efficient Online Federated Learning Framework for Nonlinear RegressionCode0
Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation SystemCode0
UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous DataCode0
A Stochastic Optimization Framework for Private and Fair Learning From Decentralized DataCode0
Federated clustering with GAN-based data synthesisCode0
Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex HullsCode0
A Federated Learning Benchmark for Drug-Target InteractionCode0
Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid Byzantines in Federated LearningCode0
A Statistical Analysis of Deep Federated Learning for Intrinsically Low-dimensional DataCode0
Federated Black-Box Adaptation for Semantic SegmentationCode0
Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced DataCode0
Adaptive Expert Models for Personalization in Federated LearningCode0
AutoDFL: A Scalable and Automated Reputation-Aware Decentralized Federated LearningCode0
FilFL: Client Filtering for Optimized Client Participation in Federated LearningCode0
Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local UpdatesCode0
Decentralised Semi-supervised Onboard Learning for Scene Classification in Low-Earth OrbitCode0
Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated LearningCode0
FissionVAE: Federated Non-IID Image Generation with Latent Space and Decoder DecompositionCode0
Federated Intelligence for Active Queue Management in Inter-Domain CongestionCode0
FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated LearningCode0
FedDWA: Personalized Federated Learning with Dynamic Weight AdjustmentCode0
Fed-ensemble: Improving Generalization through Model Ensembling in Federated LearningCode0
Communication-Efficient Federated Learning via Predictive CodingCode0
FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy LabelsCode0
FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature AugmentationCode0
FedER: Federated Learning through Experience Replay and Privacy-Preserving Data SynthesisCode0
FedDUAL: A Dual-Strategy with Adaptive Loss and Dynamic Aggregation for Mitigating Data Heterogeneity in Federated LearningCode0
FedEntropy: Efficient Device Grouping for Federated Learning Using Maximum Entropy JudgmentCode0
FedDebug: Systematic Debugging for Federated Learning ApplicationsCode0
FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive ModelsCode0
Adaptive Compression in Federated Learning via Side InformationCode0
FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue ClassificationCode0
Federated Active Learning for Target Domain GeneralisationCode0
FedCos: A Scene-adaptive Federated Optimization Enhancement for Performance ImprovementCode0
FedCore: Straggler-Free Federated Learning with Distributed CoresetsCode0
Adaptive Federated Learning in Resource Constrained Edge Computing SystemsCode0
FedTune: Automatic Tuning of Federated Learning Hyper-Parameters from System PerspectiveCode0
Fair Resource Allocation in Federated LearningCode0
FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust ClusteringCode0
FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated LearningCode0
FDA-Opt: Communication-Efficient Federated Fine-Tuning of Language ModelsCode0
Federated Causal Discovery From InterventionsCode0
Auto-weighted Robust Federated Learning with Corrupted Data SourcesCode0
FedCCRL: Federated Domain Generalization with Cross-Client Representation LearningCode0
3FM: Multi-modal Meta-learning for Federated TasksCode0
FedCert: Federated Accuracy CertificationCode0
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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