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 6501–6550 of 6771 papers

TitleStatusHype
Energizing Federated Learning via Filter-Aware Attention—0
Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computation—0
Energy-Aware Analog Aggregation for Federated Learning with Redundant Data—0
Energy-Aware Edge Association for Cluster-based Personalized Federated Learning—0
Energy-Aware Federated Learning in Satellite Constellations—0
Energy-Aware Federated Learning with Distributed User Sampling and Multichannel ALOHA—0
Energy Demand Prediction with Federated Learning for Electric Vehicle Networks—0
Energy-Efficient Channel Decoding for Wireless Federated Learning: Convergence Analysis and Adaptive Design—0
Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approach—0
Energy-Efficient Federated Learning and Migration in Digital Twin Edge Networks—0
Energy Efficient Federated Learning in Integrated Fog-Cloud Computing Enabled Internet-of-Things Networks—0
Energy-Efficient Federated Learning in Cooperative Communication within Factory Subnetworks—0
Energy Efficient Federated Learning Over Wireless Communication Networks—0
Energy Minimization for Participatory Federated Learning in IoT Analyzed via Game Theory—0
Enforcing fairness in private federated learning via the modified method of differential multipliers—0
Enhanced channel estimation for near-field IRS-aided multi-user MIMO system via a large deep residual network—0
Enhanced Decentralized Federated Learning based on Consensus in Connected Vehicles—0
Enhanced Federated Anomaly Detection Through Autoencoders Using Summary Statistics-Based Thresholding—0
Enhance Local Consistency in Federated Learning: A Multi-Step Inertial Momentum Approach—0
Enhanced Over-the-Air Federated Learning Using AI-based Fluid Antenna System—0
Enhancing Air Quality Monitoring: A Brief Review of Federated Learning Advances—0
Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling—0
Enhancing Data Provenance and Model Transparency in Federated Learning Systems - A Database Approach—0
Enhancing Equitable Access to AI in Housing and Homelessness System of Care through Federated Learning—0
Enhancing Federated Domain Adaptation with Multi-Domain Prototype-Based Federated Fine-Tuning—0
Enhancing Federated Learning Convergence with Dynamic Data Queue and Data Entropy-driven Participant Selection—0
Enhancing Federated Learning Through Secure Cluster-Weighted Client Aggregation—0
Enhancing Federated Learning with Adaptive Differential Privacy and Priority-Based Aggregation—0
Enhancing Federated Learning with Kolmogorov-Arnold Networks: A Comparative Study Across Diverse Aggregation Strategies—0
Enhancing Federated Survival Analysis through Peer-Driven Client Reputation in Healthcare—0
Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clustering—0
Enhancing Intrusion Detection In Internet Of Vehicles Through Federated Learning—0
Enhancing IoT Security Against DDoS Attacks through Federated Learning—0
Enhancing Mental Health Support through Human-AI Collaboration: Toward Secure and Empathetic AI-enabled chatbots—0
Enhancing Mutual Trustworthiness in Federated Learning for Data-Rich Smart Cities—0
Enhancing Neural Training via a Correlated Dynamics Model—0
Enhancing Object Detection with Hybrid dataset in Manufacturing Environments: Comparing Federated Learning to Conventional Techniques—0
Enhancing Performance for Highly Imbalanced Medical Data via Data Regularization in a Federated Learning Setting—0
Enhancing Privacy against Inversion Attacks in Federated Learning by using Mixing Gradients Strategies—0
Enhancing Privacy in ControlNet and Stable Diffusion via Split Learning—0
Enhancing Privacy in Federated Learning through Local Training—0
Enhancing Privacy in Federated Learning through Quantum Teleportation Integration—0
Enhancing Privacy in the Early Detection of Sexual Predators Through Federated Learning and Differential Privacy—0
Enhancing Privacy Preservation in Federated Learning via Learning Rate Perturbation—0
Enhancing Quantum Security over Federated Learning via Post-Quantum Cryptography—0
Enhancing Reliability in Federated mmWave Networks: A Practical and Scalable Solution using Radar-Aided Dynamic Blockage Recognition—0
Enhancing Scalability and Reliability in Semi-Decentralized Federated Learning With Blockchain: Trust Penalization and Asynchronous Functionality—0
Enhancing Security and Privacy in Federated Learning using Low-Dimensional Update Representation and Proximity-Based Defense—0
Enhancing Security in Federated Learning through Adaptive Consensus-Based Model Update Validation—0
Enhancing Spectrum Efficiency in 6G Satellite Networks: A GAIL-Powered Policy Learning via Asynchronous Federated Inverse Reinforcement Learning—0
Show:102550
← PrevPage 131 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