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 5701–5750 of 6771 papers

TitleStatusHype
Adaptive Gradient Clipping for Robust Federated Learning—0
Boosting the Performance of Decentralized Federated Learning via Catalyst Acceleration—0
Boosting with Multiple Sources—0
BPFISH: Blockchain and Privacy-preserving FL Inspired Smart Healthcare—0
Brain MRI Screening Tool with Federated Learning—0
Brain Storm Optimization Based Swarm Learning for Diabetic Retinopathy Image Classification—0
BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning—0
Brain Tumor Detection in MRI Based on Federated Learning with YOLOv11—0
Brave: Byzantine-Resilient and Privacy-Preserving Peer-to-Peer Federated Learning—0
Breaking Data Silos: Towards Open and Scalable Mobility Foundation Models via Generative Continual Learning—0
Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in Federated Learning—0
Breaking the centralized barrier for cross-device federated learning—0
Breaking the Memory Wall for Heterogeneous Federated Learning via Progressive Training—0
Breaking the Memory Wall for Heterogeneous Federated Learning via Model Splitting—0
BRFL: A Blockchain-based Byzantine-Robust Federated Learning Model—0
Bridging Data Barriers among Participants: Assessing the Potential of Geoenergy through Federated Learning—0
Bridging Data Islands: Geographic Heterogeneity-Aware Federated Learning for Collaborative Remote Sensing Semantic Segmentation—0
Bridging the Gap Between Foundation Models and Heterogeneous Federated Learning—0
MUDGUARD: Taming Malicious Majorities in Federated Learning using Privacy-Preserving Byzantine-Robust Clustering—0
Bristle: Decentralized Federated Learning in Byzantine, Non-i.i.d. Environments—0
Distributed Non-Convex Optimization with One-Bit Compressors on Heterogeneous Data: Efficient and Resilient Algorithms—0
Budgeted Online Model Selection and Fine-Tuning via Federated Learning—0
Budgeted Online Selection of Candidate IoT Clients to Participate in Federated Learning—0
Buffered Asynchronous Secure Aggregation for Cross-Device Federated Learning—0
Buffer is All You Need: Defending Federated Learning against Backdoor Attacks under Non-iids via Buffering—0
Building Gradient Bridges: Label Leakage from Restricted Gradient Sharing in Federated Learning—0
Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates—0
Byzantine-resilient Federated Learning Employing Normalized Gradients on Non-IID Datasets—0
Byzantine-Resilient Federated Learning at Edge—0
Byzantine-Resilient Federated Learning via Distributed Optimization—0
Byzantine-resilient Federated Learning With Adaptivity to Data Heterogeneity—0
Byzantine-Resilient High-Dimensional Federated Learning—0
Byzantine-Resilient Over-the-Air Federated Learning under Zero-Trust Architecture—0
Byzantine-Resilient Secure Aggregation for Federated Learning Without Privacy Compromises—0
Byzantine-Resilient Secure Federated Learning—0
Byzantine-Resilient Zero-Order Optimization for Communication-Efficient Heterogeneous Federated Learning—0
ByzSecAgg: A Byzantine-Resistant Secure Aggregation Scheme for Federated Learning Based on Coded Computing and Vector Commitment—0
Byzantine-Robust and Privacy-Preserving Framework for FedML—0
Byzantine-robust Decentralized Federated Learning via Dual-domain Clustering and Trust Bootstrapping—0
Byzantine-Robust Decentralized Federated Learning—0
Byzantine-Robust Federated Learning: An Overview With Focus on Developing Sybil-based Attacks to Backdoor Augmented Secure Aggregation Protocols—0
Byzantine-Robust Federated Learning over Ring-All-Reduce Distributed Computing—0
Byzantine-robust Federated Learning through Spatial-temporal Analysis of Local Model Updates—0
Byzantine-Robust Federated Learning via Credibility Assessment on Non-IID Data—0
Byzantine-Robust Federated Learning with Variance Reduction and Differential Privacy—0
Byzantine-Robust Federated Linear Bandits—0
Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging—0
Byzantines can also Learn from History: Fall of Centered Clipping in Federated Learning—0
CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balance—0
CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning—0
Show:102550
← PrevPage 115 of 136Next →
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