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 59015950 of 6771 papers

TitleStatusHype
FedExP: Speeding Up Federated Averaging via ExtrapolationCode0
SparsyFed: Sparse Adaptive Federated TrainingCode0
Federating Dynamic Models using Early-Exit Architectures for Automatic Speech Recognition on Heterogeneous ClientsCode0
FedNS: A Fast Sketching Newton-Type Algorithm for Federated LearningCode0
Differentially Private Federated Variational InferenceCode0
Learnable Sparse Customization in Heterogeneous Edge ComputingCode0
Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate GradientsCode0
Attention on Personalized Clinical Decision Support System: Federated Learning ApproachCode0
FedAPM: Federated Learning via ADMM with Partial Model PersonalizationCode0
Attacks on fairness in Federated LearningCode0
Why Batch Normalization Damage Federated Learning on Non-IID Data?Code0
FedAli: Personalized Federated Learning with Aligned Prototypes through Optimal TransportCode0
Learn How to Query from Unlabeled Data Streams in Federated LearningCode0
FedOS: using open-set learning to stabilize training in federated learningCode0
Attack-Resistant Federated Learning with Residual-based ReweightingCode0
FedAH: Aggregated Head for Personalized Federated LearningCode0
Voronoi-grid-based Pareto Front Learning and Its Application to Collaborative Federated LearningCode0
Bridging Differential Privacy and Byzantine-Robustness via Model AggregationCode0
Over-the-Air Fair Federated Learning via Multi-Objective OptimizationCode0
FedPall: Prototype-based Adversarial and Collaborative Learning for Federated Learning with Feature DriftCode0
FedPalm: A General Federated Learning Framework for Closed- and Open-Set Palmprint VerificationCode0
ATR-Bench: A Federated Learning Benchmark for Adaptation, Trust, and ReasoningCode0
Sample-based and Feature-based Federated Learning for Unconstrained and Constrained Nonconvex Optimization via Mini-batch SSCACode0
Federated Visual Classification with Real-World Data DistributionCode0
Spectral Co-Distillation for Personalized Federated LearningCode0
Differentially-Private Federated Linear BanditsCode0
Sample Complexity of Linear Regression Models for Opinion Formation in NetworksCode0
Feature Reconstruction Attacks and Countermeasures of DNN training in Vertical Federated LearningCode0
FedPAW: Federated Learning with Personalized Aggregation Weights for Urban Vehicle Speed PredictionCode0
Profit Allocation for Federated LearningCode0
Breaching FedMD: Image Recovery via Paired-Logits Inversion AttackCode0
Federated User Preference Modeling for Privacy-Preserving Cross-Domain RecommendationCode0
Learning Private Neural Language Modeling with Attentive AggregationCode0
Learning Rate Adaptation for Federated and Differentially Private LearningCode0
Brain Age Estimation Using Structural MRI: A Clustered Federated Learning ApproachCode0
Learning Task-Aware Energy Disaggregation: a Federated ApproachCode0
Federated Unlearning via Class-Discriminative PruningCode0
Learning to Backdoor Federated LearningCode0
Feature Norm Regularized Federated Learning: Transforming Skewed Distributions into Global InsightsCode0
Federated Unlearning Made Practical: Seamless Integration via Negated Pseudo-GradientsCode0
Aergia: Leveraging Heterogeneity in Federated Learning SystemsCode0
Communication-Efficient Design of Learning System for Energy Demand Forecasting of Electrical VehiclesCode0
A Joint Approach to Local Updating and Gradient Compression for Efficient Asynchronous Federated LearningCode0
FDAPT: Federated Domain-adaptive Pre-training for Language ModelsCode0
Differentially Private Federated Learning via Reconfigurable Intelligent SurfaceCode0
FCA: Taming Long-tailed Federated Medical Image Classification by Classifier AnchoringCode0
Communication-Efficient and Privacy-Adaptable Mechanism for Federated LearningCode0
Asynchronous Federated OptimizationCode0
Federated Two Stage Decoupling With Adaptive Personalization LayersCode0
FedPref: Federated Learning Across Heterogeneous Multi-objective PreferencesCode0
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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