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 62016250 of 6771 papers

TitleStatusHype
FL-Defender: Combating Targeted Attacks in Federated LearningCode0
Federated Learning via Consensus Mechanism on Heterogeneous Data: A New Perspective on ConvergenceCode0
FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature AugmentationCode0
FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature AugmentationCode0
Vertical Federated Learning with Missing Features During Training and InferenceCode0
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed TrainingCode0
Reliable Federated Disentangling Network for Non-IID Domain FeatureCode0
FLEE-GNN: A Federated Learning System for Edge-Enhanced Graph Neural Network in Analyzing Geospatial Resilience of Multicommodity Food FlowsCode0
Towards General Deep Leakage in Federated LearningCode0
FLeNS: Federated Learning with Enhanced Nesterov-Newton SketchCode0
Personalized Federated Learning via StackingCode0
When Cars meet Drones: Hyperbolic Federated Learning for Source-Free Domain Adaptation in Adverse WeatherCode0
Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and AnalysisCode0
Federated Learning under Partially Class-Disjoint Data via Manifold ReshapingCode0
Secure Federated Submodel LearningCode0
Can we Generalize and Distribute Private Representation Learning?Code0
Deep Gradient Compression Reduce the Communication Bandwidth For distributed TraningCode0
MIA-BAD: An Approach for Enhancing Membership Inference Attack and its Mitigation with Federated LearningCode0
Explainable Federated Bayesian Causal Inference and Its Application in Advanced ManufacturingCode0
MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central UpdatesCode0
Experimenting with Normalization Layers in Federated Learning on non-IID scenariosCode0
FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive ModelsCode0
Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI ModelsCode0
TrojanDam: Detection-Free Backdoor Defense in Federated Learning through Proactive Model Robustification utilizing OOD DataCode0
FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality PredictionCode0
Personalized Federated Learning with Contextual Modulation and Meta-LearningCode0
Flight: A FaaS-Based Framework for Complex and Hierarchical Federated LearningCode0
AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias EstimationCode0
Federated Learning Under Attack: Exposing Vulnerabilities through Data Poisoning Attacks in Computer NetworksCode0
RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial Data ManipulationCode0
UnifyFL: Enabling Decentralized Cross-Silo Federated LearningCode0
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and ProtectionCode0
Federated Learning on Patient Data for Privacy-Protecting Polycystic Ovary Syndrome TreatmentCode0
A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated LearningCode0
Deep Domain Isolation and Sample Clustered Federated Learning for Semantic SegmentationCode0
Mitigating Adversarial Attacks in Federated Learning with Trusted Execution EnvironmentsCode0
Mitigating Adversarial Attacks on ECG Classification in Federated Learning via Adversarial TrainingCode0
Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence GuaranteesCode0
Decentralized Quantum Federated Learning for Metaverse: Analysis, Design and ImplementationCode0
FLoCoRA: Federated learning compression with low-rank adaptationCode0
Addressing Heterogeneity in Federated Learning via Distributional TransformationCode0
FLoRA: Enhancing Vision-Language Models with Parameter-Efficient Federated LearningCode0
Federated Learning of Socially Appropriate Agent Behaviours in Simulated Home EnvironmentsCode0
Towards Hyper-parameter-free Federated LearningCode0
Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-TuningCode0
Federated Learning of Models Pre-Trained on Different Features with Consensus GraphsCode0
Experimenting with Emerging RISC-V Systems for Decentralised Machine LearningCode0
Policy-Based Federated LearningCode0
Flow: Per-Instance Personalized Federated Learning Through Dynamic RoutingCode0
FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious ClientsCode0
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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