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

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Showing 1801–1850 of 6771 papers

TitleStatusHype
Edge-assisted U-Shaped Split Federated Learning with Privacy-preserving for Internet of Things—0
Edge-cloud Collaborative Learning with Federated and Centralized Features—0
EdgeConvEns: Convolutional Ensemble Learning for Edge Intelligence—0
Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approach—0
EdgeFL: A Lightweight Decentralized Federated Learning Framework—0
Edge Intelligence Over the Air: Two Faces of Interference in Federated Learning—0
Energizing Federated Learning via Filter-Aware Attention—0
Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges—0
Data-Agnostic Model Poisoning against Federated Learning: A Graph Autoencoder Approach—0
eFedDNN: Ensemble based Federated Deep Neural Networks for Trajectory Mode Inference—0
eFedLLM: Efficient LLM Inference Based on Federated Learning—0
Effective and Efficient Cross-City Traffic Knowledge Transfer: A Privacy-Preserving Perspective—0
Effective and Efficient Federated Tree Learning on Hybrid Data—0
Effective and secure federated online learning to rank—0
DASHA: Distributed Nonconvex Optimization with Communication Compression, Optimal Oracle Complexity, and No Client Synchronization—0
Bridging Data Barriers among Participants: Assessing the Potential of Geoenergy through Federated Learning—0
Effectively Heterogeneous Federated Learning: A Pairing and Split Learning Based Approach—0
Bridging Data Islands: Geographic Heterogeneity-Aware Federated Learning for Collaborative Remote Sensing Semantic Segmentation—0
Effect of Homomorphic Encryption on the Performance of Training Federated Learning Generative Adversarial Networks—0
EFFGAN: Ensembles of fine-tuned federated GANs—0
Efficient Adaptive Federated Optimization—0
Efficient Adaptive Federated Optimization of Federated Learning for IoT—0
Age-of-Gradient Updates for Federated Learning over Random Access Channels—0
DaringFed: A Dynamic Bayesian Persuasion Pricing for Online Federated Learning under Two-sided Incomplete Information—0
Efficient and Privacy Preserving Group Signature for Federated Learning—0
Efficient and Private Federated Learning with Partially Trainable Networks—0
Efficient and Reliable Overlay Networks for Decentralized Federated Learning—0
An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning—0
Efficient and Secure Federated Learning for Financial Applications—0
Efficient Asynchronous Federated Learning with Sparsification and Quantization—0
Efficient Batch Homomorphic Encryption for Vertically Federated XGBoost—0
Efficient Classification of SARS-CoV-2 Spike Sequences Using Federated Learning—0
Efficient Client Contribution Evaluation for Horizontal Federated Learning—0
Efficient Client Selection in Federated Learning—0
Efficient Cluster Selection for Personalized Federated Learning: A Multi-Armed Bandit Approach—0
Efficient Collaborations through Weight-Driven Coalition Dynamics in Federated Learning Systems—0
Efficient Conformal Prediction under Data Heterogeneity—0
Efficient Core-selecting Incentive Mechanism for Data Sharing in Federated Learning—0
Efficient Cross-Domain Federated Learning by MixStyle Approximation—0
Efficient Data Distribution Estimation for Accelerated Federated Learning—0
Efficient Data Valuation Approximation in Federated Learning: A Sampling-based Approach—0
Efficient Deployment of Large Language Models on Resource-constrained Devices—0
Efficient Device Scheduling with Multi-Job Federated Learning—0
Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors—0
Adaptive Differential Filters for Fast and Communication-Efficient Federated Learning—0
Efficient Federated Finetuning of Tiny Transformers with Resource-Constrained Devices—0
Byzantine-resilient Federated Learning Employing Normalized Gradients on Non-IID Datasets—0
An Evaluation of Non-Contrastive Self-Supervised Learning for Federated Medical Image Analysis—0
Efficient Federated Learning for AIoT Applications Using Knowledge Distillation—0
Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computation—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