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 2201–2250 of 6771 papers

TitleStatusHype
DWFL: Enhancing Federated Learning through Dynamic Weighted Averaging—0
DVFL: A Vertical Federated Learning Method for Dynamic Data—0
DU-Shapley: A Shapley Value Proxy for Efficient Dataset Valuation—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
Addressing Heterogeneity in Federated Learning: Challenges and Solutions for a Shared Production Environment—0
A Blockchain Solution for Collaborative Machine Learning over IoT—0
Dubhe: Towards Data Unbiasedness with Homomorphic Encryption in Federated Learning Client Selection—0
Dual-Segment Clustering Strategy for Hierarchical Federated Learning in Heterogeneous Wireless Environments—0
BlockFLow: An Accountable and Privacy-Preserving Solution for Federated Learning—0
Learning to Prompt Your Domain for Vision-Language Models—0
BlockFLA: Accountable Federated Learning via Hybrid Blockchain Architecture—0
Dual Model Replacement:invisible Multi-target Backdoor Attack based on Federal Learning—0
DualGFL: Federated Learning with a Dual-Level Coalition-Auction Game—0
DualFL: A Duality-based Federated Learning Algorithm with Communication Acceleration in the General Convex Regime—0
Block-FeDL: Electric Vehicle Charging Load Forecasting using Federated Learning and Blockchain—0
An Efficient and Robust System for Vertically Federated Random Forest—0
Blockchain-enabled Trustworthy Federated Unlearning—0
Dual-Criterion Model Aggregation in Federated Learning: Balancing Data Quantity and Quality—0
dsLassoCov: a federated machine learning approach incorporating covariate control—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
Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning—0
Drift-Aware Federated Learning: A Causal Perspective—0
DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup—0
Blockchain-Enabled Federated Learning Approach for Vehicular Networks—0
DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing—0
DRAG: Divergence-based Adaptive Aggregation in Federated learning on Non-IID Data—0
Blockchain-enabled Clustered and Scalable Federated Learning (BCS-FL) Framework in UAV Networks—0
DRAGD: A Federated Unlearning Data Reconstruction Attack Based on Gradient Differences—0
DRACO: Decentralized Asynchronous Federated Learning over Row-Stochastic Wireless Networks—0
Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness—0
DQRE-SCnet: A novel hybrid approach for selecting users in Federated Learning with Deep-Q-Reinforcement Learning based on Spectral Clustering—0
DPZV: Elevating the Tradeoff between Privacy and Utility in Zeroth-Order Vertical Federated Learning—0
An Autoencoder-Based Constellation Design for AirComp in Wireless Federated Learning—0
Addressing Data Heterogeneity in Federated Learning of Cox Proportional Hazards Models—0
DPVS-Shapley:Faster and Universal Contribution Evaluation Component in Federated Learning—0
Blockchain-Empowered Cyber-Secure Federated Learning for Trustworthy Edge Computing—0
DP-REC: Private & Communication-Efficient Federated Learning—0
DPP-based Client Selection for Federated Learning with Non-IID Data—0
Blockchained Federated Learning for Threat Defense—0
An Auction-based Marketplace for Model Trading in Federated Learning—0
On the Convergence of Differentially Private Federated Learning on Non-Lipschitz Objectives, and with Normalized Client Updates—0
DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation—0
Blockchained Federated Learning for Internet of Things: A Comprehensive Survey—0
DPCOVID: Privacy-Preserving Federated Covid-19 Detection—0
Blockchain-based Trustworthy Federated Learning Architecture—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