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 16011650 of 6771 papers

TitleStatusHype
FedAVO: Improving Communication Efficiency in Federated Learning with African Vultures OptimizerCode0
Feature Norm Regularized Federated Learning: Transforming Skewed Distributions into Global InsightsCode0
Feature Reconstruction Attacks and Countermeasures of DNN training in Vertical Federated LearningCode0
On-Device Collaborative Language Modeling via a Mixture of Generalists and SpecialistsCode0
FedAH: Aggregated Head for Personalized Federated LearningCode0
Differentially private partitioned variational inferenceCode0
BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoTCode0
Improving Differentially Private SGD via Randomly Sparsified GradientsCode0
FDAPT: Federated Domain-adaptive Pre-training for Language ModelsCode0
Federated Learning with Convex Global and Local ConstraintsCode0
One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model DiversityCode0
On Noisy Evaluation in Federated Hyperparameter TuningCode0
CollaFuse: Navigating Limited Resources and Privacy in Collaborative Generative AICode0
FCA: Taming Long-tailed Federated Medical Image Classification by Classifier AnchoringCode0
FedBrain-Distill: Communication-Efficient Federated Brain Tumor Classification Using Ensemble Knowledge Distillation on Non-IID DataCode0
On the Convergence of Federated Learning Algorithms without Data SimilarityCode0
CollaFuse: Collaborative Diffusion ModelsCode0
On the Efficiency of Privacy Attacks in Federated LearningCode0
Collaborative Unsupervised Visual Representation Learning from Decentralized DataCode0
Collaborative Training of Medical Artificial Intelligence Models with non-uniform LabelsCode0
On the Robustness of Distributed Machine Learning against Transfer AttacksCode0
FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial LearningCode0
FairFML: Fair Federated Machine Learning with a Case Study on Reducing Gender Disparities in Cardiac Arrest Outcome PredictionCode0
Aergia: Leveraging Heterogeneity in Federated Learning SystemsCode0
Fairness and Privacy in Federated Learning and Their Implications in HealthcareCode0
FAIR-FATE: Fair Federated Learning with MomentumCode0
F3: Fair and Federated Face Attribute Classification with Heterogeneous DataCode0
FADAS: Towards Federated Adaptive Asynchronous OptimizationCode0
FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?Code0
Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement LearningCode0
FAA-CLIP: Federated Adversarial Adaptation of CLIPCode0
Exploring Selective Layer Fine-Tuning in Federated LearningCode0
A Privacy-Preserving Approach to Extraction of Personal Information through Automatic Annotation and Federated LearningCode0
FACT: Federated Adversarial Cross TrainingCode0
FBFL: A Field-Based Coordination Approach for Data Heterogeneity in Federated LearningCode0
FedBug: A Bottom-Up Gradual Unfreezing Framework for Federated LearningCode0
Explicit Personalization and Local Training: Double Communication Acceleration in Federated LearningCode0
Experimenting with Normalization Layers in Federated Learning on non-IID scenariosCode0
Explainable Federated Bayesian Causal Inference and Its Application in Advanced ManufacturingCode0
Cross-Silo Heterogeneous Model Federated Multitask LearningCode0
Exact Penalty Method for Federated LearningCode0
PathFL: Multi-Alignment Federated Learning for Pathology Image SegmentationCode0
CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat DetectionCode0
Semi-Supervised Federated Peer Learning for Skin Lesion ClassificationCode0
Experimenting with Emerging RISC-V Systems for Decentralised Machine LearningCode0
Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor SegmentationCode0
Evaluating Federated Kolmogorov-Arnold Networks on Non-IID DataCode0
Estimation of Microphone Clusters in Acoustic Sensor Networks using Unsupervised Federated LearningCode0
Approximate Gradient Coding for Privacy-Flexible Federated Learning with Non-IID DataCode0
Equipping Federated Graph Neural Networks with Structure-aware Group FairnessCode0
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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