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 65016550 of 6771 papers

TitleStatusHype
Federated Document Visual Question Answering: A Pilot StudyCode0
Towards Privacy-Aware Causal Structure Learning in Federated SettingCode0
Enhancing Heterogeneous Federated Learning with Knowledge Extraction and Multi-Model FusionCode0
An Element-Wise Weights Aggregation Method for Federated LearningCode0
A Statistical Analysis of Deep Federated Learning for Intrinsically Low-dimensional DataCode0
Clustered Federated Learning via Generalized Total Variation MinimizationCode0
Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous DrivingCode0
Heterogeneous Federated Learning with Prototype Alignment and UpscalingCode0
Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication ComplexityCode0
Adaptive Federated Learning in Resource Constrained Edge Computing SystemsCode0
HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated LearningCode0
Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data MiningCode0
Network-Level Adversaries in Federated LearningCode0
HFedMS: Heterogeneous Federated Learning with Memorable Data Semantics in Industrial MetaverseCode0
VFLGAN: Vertical Federated Learning-based Generative Adversarial Network for Vertically Partitioned Data PublicationCode0
Auto-weighted Robust Federated Learning with Corrupted Data SourcesCode0
Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated LearningCode0
Communication-Efficient Zeroth-Order Distributed Online Optimization: Algorithm, Theory, and ApplicationsCode0
HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for ElectroencephalographyCode0
Federated Stochastic Gradient Langevin DynamicsCode0
Effective Heterogeneous Federated Learning via Efficient Hypernetwork-based Weight GenerationCode0
Federated Continual Learning for Text Classification via Selective Inter-client TransferCode0
Communication-efficient Vertical Federated Learning via Compressed Error FeedbackCode0
ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised LearningCode0
Client2Vec: Improving Federated Learning by Distribution Shifts Aware Client IndexingCode0
Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the TopCode0
NIPD: A Federated Learning Person Detection Benchmark Based on Real-World Non-IID DataCode0
Client-Edge-Cloud Hierarchical Federated LearningCode0
Hierarchical Federated Learning in Multi-hop Cluster-Based VANETsCode0
EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated LearningCode0
No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID DataCode0
Synergizing Foundation Models and Federated Learning: A SurveyCode0
Hierarchical Federated Learning with Multi-Timescale Gradient CorrectionCode0
Noise-Aware Algorithm for Heterogeneous Differentially Private Federated LearningCode0
Towards Federated Learning With Byzantine-Robust Client WeightingCode0
Semi-Decentralized Federated Learning with Cooperative D2D Local Model AggregationsCode0
A Distributed Generative AI Approach for Heterogeneous Multi-Domain Environments under Data Sharing constraintsCode0
Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneityCode0
Certified Robustness in Federated LearningCode0
Central Server Free Federated Learning over Single-sided Trust Social NetworksCode0
PRISM: Privacy-Preserving Improved Stochastic Masking for Federated Generative ModelsCode0
Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation PerspectiveCode0
Towards Efficient Synchronous Federated Training: A Survey on System Optimization StrategiesCode0
Privacy Amplification by DecentralizationCode0
Privacy Amplification for Federated Learning via User Sampling and Wireless AggregationCode0
High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributionsCode0
Non-parametric regularization for class imbalance federated medical image classificationCode0
FedTune: Automatic Tuning of Federated Learning Hyper-Parameters from System PerspectiveCode0
Early prediction of the risk of ICU mortality with Deep Federated LearningCode0
Share Secrets for Privacy: Confidential Forecasting with Vertical Federated LearningCode0
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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