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 5651–5700 of 6771 papers

TitleStatusHype
Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients—0
BF-Meta: Secure Blockchain-enhanced Privacy-preserving Federated Learning for Metaverse—0
BFV-Based Homomorphic Encryption for Privacy-Preserving CNN Models—0
Biased Federated Learning under Wireless Heterogeneity—0
Biased Over-the-Air Federated Learning under Wireless Heterogeneity—0
Bias-Eliminating Augmentation Learning for Debiased Federated Learning—0
Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning—0
BICompFL: Stochastic Federated Learning with Bi-Directional Compression—0
Binary Federated Learning with Client-Level Differential Privacy—0
Bit-aware Randomized Response for Local Differential Privacy in Federated Learning—0
Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization—0
Blind Federated Learning via Over-the-Air q-QAM—0
Blind Federated Learning without initial model—0
BlindFL: Vertical Federated Machine Learning without Peeking into Your Data—0
Fabricated Flips: Poisoning Federated Learning without Data—0
Label Inference Attacks against Node-level Vertical Federated GNNs—0
Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing—0
Blockchain Assisted Decentralized Federated Learning (BLADE-FL) with Lazy Clients—0
Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation—0
Blockchain-Based Federated Learning: Incentivizing Data Sharing and Penalizing Dishonest Behavior—0
A Systematic Survey of Blockchained Federated Learning—0
Blockchain-based Federated Learning for Failure Detection in Industrial IoT—0
Blockchain-based Federated Learning for Decentralized Energy Management Systems—0
Blockchain-Based Federated Learning in Mobile Edge Networks with Application in Internet of Vehicles—0
Blockchain-based Federated Learning with Secure Aggregation in Trusted Execution Environment for Internet-of-Things—0
Blockchain-based Framework for Scalable and Incentivized Federated Learning—0
Blockchain-based Monitoring for Poison Attack Detection in Decentralized Federated Learning—0
Blockchain-based Optimized Client Selection and Privacy Preserved Framework for Federated Learning—0
Blockchain-based Secure Client Selection in Federated Learning—0
Blockchain-based Trustworthy Federated Learning Architecture—0
Blockchained Federated Learning for Internet of Things: A Comprehensive Survey—0
Blockchained Federated Learning for Threat Defense—0
Blockchain-Empowered Cyber-Secure Federated Learning for Trustworthy Edge Computing—0
Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness—0
Blockchain-enabled Clustered and Scalable Federated Learning (BCS-FL) Framework in UAV Networks—0
Blockchain-Enabled Federated Learning Approach for Vehicular Networks—0
Blockchain-Enabled Federated Learning: A Reference Architecture Design, Implementation, and Verification—0
Blockchain-Enabled Privacy-Preserving Second-Order Federated Edge Learning in Personalized Healthcare—0
Blockchain-enabled Trustworthy Federated Unlearning—0
Block-FeDL: Electric Vehicle Charging Load Forecasting using Federated Learning and Blockchain—0
BlockFLA: Accountable Federated Learning via Hybrid Blockchain Architecture—0
BlockFLow: An Accountable and Privacy-Preserving Solution for Federated Learning—0
Blood Glucose Level Prediction: A Graph-based Explainable Method with Federated Learning—0
BN-SCAFFOLD: controlling the drift of Batch Normalization statistics in Federated Learning—0
BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning—0
Boost Decentralized Federated Learning in Vehicular Networks by Diversifying Data Sources—0
Boosting Fairness and Robustness in Over-the-Air Federated Learning—0
Boosting Federated Domain Generalization: Understanding the Role of Advanced Pre-Trained Architectures—0
Boosting Federated Learning Convergence with Prototype Regularization—0
Boosting multi-demographic federated learning for chest x-ray analysis using general-purpose self-supervised representations—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