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 63016350 of 6771 papers

TitleStatusHype
A New Perspective on Privacy Protection in Federated Learning with Granular-Ball ComputingCode0
Vertical Federated Unlearning via Backdoor CertificationCode0
Freezing of Gait Detection Using Gramian Angular Fields and Federated Learning from Wearable SensorsCode0
Decentralised Semi-supervised Onboard Learning for Scene Classification in Low-Earth OrbitCode0
Frequency-Based Federated Domain Generalization for Polyp SegmentationCode0
Equipping Federated Graph Neural Networks with Structure-aware Group FairnessCode0
Fair Resource Allocation in Federated LearningCode0
pFedAFM: Adaptive Feature Mixture for Batch-Level Personalization in Heterogeneous Federated LearningCode0
Dealing With Heterogeneous 3D MR Knee Images: A Federated Few-Shot Learning Method With Dual Knowledge DistillationCode0
Real-World Image Datasets for Federated LearningCode0
Three Approaches for Personalization with Applications to Federated LearningCode0
A Survey on Contribution Evaluation in Vertical Federated LearningCode0
A Survey of Incremental Transfer Learning: Combining Peer-to-Peer Federated Learning and Domain Incremental Learning for Multicenter CollaborationCode0
Security Assessment of Hierarchical Federated Deep LearningCode0
Model-Agnostic Federated LearningCode0
EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous DataCode0
pFedMMA: Personalized Federated Fine-Tuning with Multi-Modal Adapter for Vision-Language ModelsCode0
Ensemble Distillation for Robust Model Fusion in Federated LearningCode0
Model-based Federated Learning for Accurate MR Image Reconstruction from Undersampled k-space DataCode0
Enhanced Security and Privacy via Fragmented Federated LearningCode0
Clustered Federated Learning via Embedding DistributionsCode0
From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated LearningCode0
Federated Learning From Big Data Over NetworksCode0
Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy ConstraintsCode0
Model Fusion with Kullback--Leibler DivergenceCode0
Energy Prediction using Federated LearningCode0
Modeling Inter-Intra Heterogeneity for Graph Federated LearningCode0
FSL-SAGE: Accelerating Federated Split Learning via Smashed Activation Gradient EstimationCode0
An Equivalence Between Data Poisoning and Byzantine Gradient AttacksCode0
TIFeD: a Tiny Integer-based Federated learning algorithm with Direct feedback alignmentCode0
FTA-FTL: A Fine-Tuned Aggregation Federated Transfer Learning Scheme for Lithology Microscopic Image ClassificationCode0
Model Pruning Enables Efficient Federated Learning on Edge DevicesCode0
Addressing Data Quality Decompensation in Federated Learning via Dynamic Client SelectionCode0
Energy-Efficient Federated Learning for AIoT using Clustering MethodsCode0
Clustered Federated Learning based on Nonconvex Pairwise FusionCode0
FUNAvg: Federated Uncertainty Weighted Averaging for Datasets with Diverse LabelsCode0
Towards Model-Agnostic Federated Learning over NetworksCode0
Data Subsampling for Bayesian Neural NetworksCode0
Federated learning framework for collaborative remaining useful life prognostics: an aircraft engine case studyCode0
Data Leakage in Federated AveragingCode0
ModularFed: Leveraging Modularity in Federated Learning FrameworksCode0
Fusion of Global and Local Knowledge for Personalized Federated LearningCode0
Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoTCode0
Data-Free Diversity-Based Ensemble Selection For One-Shot Federated Learning in Machine Learning Model MarketCode0
PGFed: Personalize Each Client's Global Objective for Federated LearningCode0
Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New AlgorithmsCode0
Game of Gradients: Mitigating Irrelevant Clients in Federated LearningCode0
CrowdGuard: Federated Backdoor Detection in Federated LearningCode0
subMFL: Compatiple subModel Generation for Federated Learning in Device Heterogenous EnvironmentCode0
Dancing in the Dark: Private Multi-Party Machine Learning in an Untrusted SettingCode0
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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