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 47014750 of 6771 papers

TitleStatusHype
FAIR-FATE: Fair Federated Learning with MomentumCode0
Semi-Synchronous Personalized Federated Learning over Mobile Edge Networks0
FaRO 2: an Open Source, Configurable Smart City Framework for Real-Time Distributed Vision and Biometric Systems0
An Energy Optimized Specializing DAG Federated Learning based on Event Triggered Communication0
Knowledge-aided Federated Learning for Energy-limited Wireless Networks0
On the Stability Analysis of Open Federated Learning Systems0
Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated LearningCode0
Communication-Efficient Federated Learning Using Censored Heavy Ball Descent0
Privacy-Preserving Online Content Moderation: A Federated Learning Use Case0
Differentially private partitioned variational inferenceCode0
Defending against Poisoning Backdoor Attacks on Federated Meta-learning0
UAV-Assisted Hierarchical Aggregation for Over-the-Air Federated Learning0
A One-shot Framework for Distributed Clustered Learning in Heterogeneous Environments0
Enhanced Decentralized Federated Learning based on Consensus in Connected Vehicles0
Federated Learning from Pre-Trained Models: A Contrastive Learning Approach0
Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks0
FedFOR: Stateless Heterogeneous Federated Learning with First-Order RegularizationCode0
FedToken: Tokenized Incentives for Data Contribution in Federated Learning0
Heterogeneous Federated Learning on a Graph0
A Secure Healthcare 5.0 System Based on Blockchain Technology Entangled with Federated Learning Technique0
Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture Model using Subspace Perturbation0
Contrastive Re-localization and History Distillation in Federated CMR Segmentation0
The Cost of Training Machine Learning Models over Distributed Data SourcesCode0
Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approach0
Federated Pruning: Improving Neural Network Efficiency with Federated Learning0
Scheduling Algorithms for Federated Learning with Minimal Energy Consumption0
Investigating the Predictive Reproducibility of Federated Graph Neural Networks using Medical DatasetsCode0
Concealing Sensitive Samples against Gradient Leakage in Federated LearningCode0
Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning using Independent Component Analysis0
Towards More Efficient Data Valuation in Healthcare Federated Learning using Ensembling0
Personalized Federated Learning with Communication Compression0
Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillation0
Anomaly Detection through Unsupervised Federated LearningCode0
FADE: Enabling Federated Adversarial Training on Heterogeneous Resource-Constrained Edge Devices0
A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated LearningCode0
FedDAR: Federated Domain-Aware Representation Learning0
Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks0
Modular Federated Learning0
Cerberus: Exploring Federated Prediction of Security Events0
Faster federated optimization under second-order similarity0
Federated Zero-Shot Learning for Visual Recognition0
Federated Transfer Learning with Multimodal Data0
Boost Decentralized Federated Learning in Vehicular Networks by Diversifying Data Sources0
Suppressing Noise from Built Environment Datasets to Reduce Communication Rounds for Convergence of Federated Learning0
FedAR+: A Federated Learning Approach to Appliance Recognition with Mislabeled Data in Residential Buildings0
Federated XGBoost on Sample-Wise Non-IID Data0
Predictive GAN-powered Multi-Objective Optimization for Hybrid Federated Split Learning0
Versatile Single-Loop Method for Gradient Estimator: First and Second Order Optimality, and its Application to Federated Learning0
Trading Off Privacy, Utility and Efficiency in Federated Learning0
To Store or Not? Online Data Selection for Federated Learning with Limited Storage0
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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