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 151200 of 6771 papers

TitleStatusHype
Ferrari: Federated Feature Unlearning via Optimizing Feature SensitivityCode1
Overcoming Data and Model Heterogeneities in Decentralized Federated Learning via Synthetic AnchorsCode1
Harmonizing Generalization and Personalization in Federated Prompt LearningCode1
Feature-based Federated Transfer Learning: Communication Efficiency, Robustness and PrivacyCode1
FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based OptimizationCode1
ACTION: Augmentation and Computation Toolbox for Brain Network Analysis with Functional MRICode1
Federated Adaptation for Foundation Model-based RecommendationsCode1
From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness MatchingCode1
Beyond Traditional Threats: A Persistent Backdoor Attack on Federated LearningCode1
EHRFL: Federated Learning Framework for Institution-Specific Model Construction using Electronic Health RecordsCode1
Towards Multi-modal Transformers in Federated LearningCode1
FedPFT: Federated Proxy Fine-Tuning of Foundation ModelsCode1
FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-TuningCode1
Dual-Personalizing Adapter for Federated Foundation ModelsCode1
Text-Enhanced Data-free Approach for Federated Class-Incremental LearningCode1
FedFisher: Leveraging Fisher Information for One-Shot Federated LearningCode1
Federated Modality-specific Encoders and Multimodal Anchors for Personalized Brain Tumor SegmentationCode1
Pencil: Private and Extensible Collaborative Learning without the Non-Colluding AssumptionCode1
FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal DecouplingCode1
FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-supervised Medical Image SegmentationCode1
Enhancing One-Shot Federated Learning Through Data and Ensemble Co-BoostingCode1
FedKit: Enabling Cross-Platform Federated Learning for Android and iOSCode1
FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter SharingCode1
Training Heterogeneous Client Models using Knowledge Distillation in Serverless Federated LearningCode1
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-offCode1
FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated LearningCode1
FedAA: A Reinforcement Learning Perspective on Adaptive Aggregation for Fair and Robust Federated LearningCode1
Group Distributionally Robust Dataset Distillation with Risk MinimizationCode1
Towards Eliminating Hard Label Constraints in Gradient Inversion AttacksCode1
Workflow Optimization for Parallel Split LearningCode1
MP-SL: Multihop Parallel Split LearningCode1
Prompt-enhanced Federated Content Representation Learning for Cross-domain RecommendationCode1
Communication-Efficient Federated Learning through Adaptive Weight Clustering and Server-Side DistillationCode1
GI-PIP: Do We Require Impractical Auxiliary Dataset for Gradient Inversion Attacks?Code1
Communication Efficient and Provable Federated UnlearningCode1
FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data GenerationCode1
Relaxed Contrastive Learning for Federated LearningCode1
Federated Unlearning: A Survey on Methods, Design Guidelines, and Evaluation MetricsCode1
Improving Transferability of Network Intrusion Detection in a Federated Learning SetupCode1
PILoRA: Prototype Guided Incremental LoRA for Federated Class-Incremental LearningCode1
FedAS: Bridging Inconsistency in Personalized Federated LearningCode1
FedHCA2: Towards Hetero-Client Federated Multi-Task LearningCode1
Federated Class-Incremental Learning with New-Class Augmented Self-DistillationCode1
Federated Learning via Input-Output Collaborative DistillationCode1
TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron ProvenanceCode1
Towards Fair Graph Federated Learning via Incentive MechanismsCode1
Federated Learning with Extremely Noisy Clients via Negative DistillationCode1
FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation NoiseCode1
Privacy-Aware Document Visual Question AnsweringCode1
CLIP-guided Federated Learning on Heterogeneous and Long-Tailed 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