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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1051–1100 of 6771 papers

TitleStatusHype
Boosting the Performance of Decentralized Federated Learning via Catalyst Acceleration—0
Adaptive Gradient Clipping for Robust Federated Learning—0
Addressing Heterogeneity in Federated Load Forecasting with Personalization Layers—0
Boosting multi-demographic federated learning for chest x-ray analysis using general-purpose self-supervised representations—0
Boosting Federated Learning Convergence with Prototype Regularization—0
An Efficient Virtual Data Generation Method for Reducing Communication in Federated Learning—0
Competitive Advantage Attacks to Decentralized Federated Learning—0
Completely Heterogeneous Federated Learning—0
Boosting Federated Domain Generalization: Understanding the Role of Advanced Pre-Trained Architectures—0
Boosting Fairness and Robustness in Over-the-Air Federated Learning—0
An Efficient Industrial Federated Learning Framework for AIoT: A Face Recognition Application—0
Boost Decentralized Federated Learning in Vehicular Networks by Diversifying Data Sources—0
An Efficient Imbalance-Aware Federated Learning Approach for Wearable Healthcare with Autoregressive Ratio Observation—0
Addressing Heterogeneity in Federated Learning: Challenges and Solutions for a Shared Production Environment—0
BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning—0
A Blockchain Solution for Collaborative Machine Learning over IoT—0
BN-SCAFFOLD: controlling the drift of Batch Normalization statistics in Federated Learning—0
Blood Glucose Level Prediction: A Graph-based Explainable Method with Federated Learning—0
Accelerated Distributed Stochastic Non-Convex Optimization over Time-Varying Directed Networks—0
Comparison of Privacy-Preserving Distributed Deep Learning Methods in Healthcare—0
Completion Time Minimization of Fog-RAN-Assisted Federated Learning With Rate-Splitting Transmission—0
BlockFLow: An Accountable and Privacy-Preserving Solution for Federated Learning—0
BlockFLA: Accountable Federated Learning via Hybrid Blockchain Architecture—0
An Efficient and Robust System for Vertically Federated Random Forest—0
Block-FeDL: Electric Vehicle Charging Load Forecasting using Federated Learning and Blockchain—0
Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning—0
Blockchain-enabled Trustworthy Federated Unlearning—0
An Efficient and Multi-private Key Secure Aggregation for Federated Learning—0
Blockchain-Enabled Privacy-Preserving Second-Order Federated Edge Learning in Personalized Healthcare—0
Blockchain-Enabled Federated Learning: A Reference Architecture Design, Implementation, and Verification—0
An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee—0
Addressing Data Heterogeneity in Federated Learning with Adaptive Normalization-Free Feature Recalibration—0
A blockchain-orchestrated Federated Learning architecture for healthcare consortia—0
Blockchain-Enabled Federated Learning Approach for Vehicular Networks—0
Blockchain-enabled Clustered and Scalable Federated Learning (BCS-FL) Framework in UAV Networks—0
Addressing Data Heterogeneity in Federated Learning of Cox Proportional Hazards Models—0
Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness—0
An Autoencoder-Based Constellation Design for AirComp in Wireless Federated Learning—0
Achieving Linear Speedup in Non-IID Federated Bilevel Learning—0
Compare Where It Matters: Using Layer-Wise Regularization To Improve Federated Learning on Heterogeneous Data—0
Blockchain-Empowered Cyber-Secure Federated Learning for Trustworthy Edge Computing—0
Blockchained Federated Learning for Threat Defense—0
An Auction-based Marketplace for Model Trading in Federated Learning—0
Blockchained Federated Learning for Internet of Things: A Comprehensive Survey—0
Blockchain-based Trustworthy Federated Learning Architecture—0
Anatomical 3D Style Transfer Enabling Efficient Federated Learning with Extremely Low Communication Costs—0
Addressing Client Drift in Federated Continual Learning with Adaptive Optimization—0
Comparing Federated Stochastic Gradient Descent and Federated Averaging for Predicting Hospital Length of Stay—0
Comparing privacy notions for protection against reconstruction attacks in machine learning—0
Complex-valued Federated Learning with Differential Privacy and MRI Applications—0
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All datasetsCityscapes heterogeneousLandmarks-User-160kCIFAR-100 (alpha=0, 10 clients per round)CIFAR-100 (alpha=0, 20 clients per round)CIFAR-100 (alpha=0.5, 10 clients per round)CIFAR-100 (alpha=0.5, 20 clients per round)CIFAR-100 (alpha=0.5, 5 clients per round)CIFAR-100 (alpha=0, 5 clients per round)CIFAR-100 (alpha=1000, 10 clients per round)CIFAR-100 (alpha=1000, 20 clients per round)CIFAR-100 (alpha=1000, 5 clients per round)CIFAR100 (alpha=0.3, 10 clients per round)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SiloBN + ASAMmIoU49.75—Unverified
2SiloBN + SAMmIoU49.1—Unverified
3SiloBNmIoU45.96—Unverified
4FedSAM + SWAmIoU43.42—Unverified
5FedASAM + SWAmIoU43.02—Unverified
6FedAvg + SWAmIoU42.48—Unverified
7FedASAMmIoU42.27—Unverified
8FedSAMmIoU41.22—Unverified
9FedAvgmIoU38.65—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAAcc@1-1262Clients68.32—Unverified
2FedSAM + SWAAcc@1-1262Clients68.12—Unverified
3FedAvg + SWAAcc@1-1262Clients67.52—Unverified
4FedASAMAcc@1-1262Clients64.23—Unverified
5FedSAMAcc@1-1262Clients63.72—Unverified
6FedAvgAcc@1-1262Clients61.91—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.64—Unverified
2FedASAMACC@1-100Clients39.76—Unverified
3FedSAM + SWAACC@1-100Clients39.51—Unverified
4FedSAMACC@1-100Clients36.93—Unverified
5FedAvgACC@1-100Clients36.74—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients41.62—Unverified
2FedASAMACC@1-100Clients40.81—Unverified
3FedSAM + SWAACC@1-100Clients39.24—Unverified
4FedAvgACC@1-100Clients38.59—Unverified
5FedSAMACC@1-100Clients38.56—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.72—Unverified
2FedSAM + SWAACC@1-100Clients46.76—Unverified
3FedASAMACC@1-100Clients46.58—Unverified
4FedSAMACC@1-100Clients44.84—Unverified
5FedAvgACC@1-100Clients41.27—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.27—Unverified
2FedASAMACC@1-100Clients47.78—Unverified
3FedSAM + SWAACC@1-100Clients46.47—Unverified
4FedSAMACC@1-100Clients46.05—Unverified
5FedAvgACC@1-100Clients42.17—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients49.17—Unverified
2FedSAM + SWAACC@1-100Clients47.96—Unverified
3FedASAMACC@1-100Clients45.61—Unverified
4FedSAMACC@1-100Clients44.73—Unverified
5FedAvgACC@1-100Clients40.43—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.01—Unverified
2FedSAM + SWAACC@1-100Clients39.3—Unverified
3FedASAMACC@1-100Clients36.04—Unverified
4FedSAMACC@1-100Clients31.04—Unverified
5FedAvgACC@1-100Clients30.25—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.97—Unverified
2FedASAM + SWAACC@1-100Clients54.79—Unverified
3FedSAM + SWAACC@1-100Clients53.67—Unverified
4FedSAMACC@1-100Clients53.39—Unverified
5FedAvgACC@1-100Clients50.25—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.5—Unverified
2FedSAM + SWAACC@1-100Clients54.36—Unverified
3FedASAM + SWAACC@1-100Clients54.1—Unverified
4FedSAMACC@1-100Clients53.97—Unverified
5FedAvgACC@1-100Clients50.66—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.81—Unverified
2FedSAMACC@1-100Clients54.01—Unverified
3FedSAM + SWAACC@1-100Clients53.9—Unverified
4FedASAM + SWAACC@1-100Clients53.86—Unverified
5FedAvgACC@1-100Clients49.92—Unverified
#ModelMetricClaimedVerifiedStatus
1AdaBestAverage Top-1 Accuracy56.2—Unverified