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 2801–2850 of 6771 papers

TitleStatusHype
FedCCL: Federated Dual-Clustered Feature Contrast Under Domain Heterogeneity—0
PraFFL: A Preference-Aware Scheme in Fair Federated LearningCode0
Federated Optimization with Doubly Regularized Drift Correction—0
FLoRA: Enhancing Vision-Language Models with Parameter-Efficient Federated LearningCode0
Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing—0
Bayesian Federated Model Compression for Communication and Computation Efficiency—0
FedAuxHMTL: Federated Auxiliary Hard-Parameter Sharing Multi-Task Learning for Network Edge Traffic Classification—0
Logit Calibration and Feature Contrast for Robust Federated Learning on Non-IID Data—0
Improving Multi-Center Generalizability of GAN-Based Fat Suppression using Federated Learning—0
The Sandwich meta-framework for architecture agnostic deep privacy-preserving transfer learning for non-invasive brainwave decoding—0
Dynamic D2D-Assisted Federated Learning over O-RAN: Performance Analysis, MAC Scheduler, and Asymmetric User Selection—0
Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models—0
Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid Byzantines in Federated LearningCode0
Federated learning model for predicting major postoperative complications—0
Collaborative Multi-source Domain Adaptation Through Optimal Transport—0
Open-Source AI-based SE Tools: Opportunities and Challenges of Collaborative Software Learning—0
SoK: On Gradient Leakage in Federated Learning—0
PlateSegFL: A Privacy-Preserving License Plate Detection Using Federated Segmentation Learning—0
Hyperparameter Optimization for SecureBoost via Constrained Multi-Objective Federated Learning—0
Vanishing Variance Problem in Fully Decentralized Neural-Network Systems—0
Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning—0
Distributionally Robust Alignment for Medical Federated Vision-Language Pre-training Under Data Heterogeneity—0
Prompt Public Large Language Models to Synthesize Data for Private On-device Applications—0
Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach—0
Approximate Gradient Coding for Privacy-Flexible Federated Learning with Non-IID DataCode0
Exploring Lightweight Federated Learning for Distributed Load Forecasting—0
An Interpretable Client Decision Tree Aggregation process for Federated LearningCode0
QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detection—0
Optimal Batch Allocation for Wireless Federated Learning—0
Robust Federated Learning for Wireless Networks: A Demonstration with Channel Estimation—0
Federated Computing -- Survey on Building Blocks, Extensions and Systems—0
Federated Multi-Agent Mapping for Planetary Exploration—0
Federated Distillation: A Survey—0
Addressing Heterogeneity in Federated Load Forecasting with Personalization Layers—0
Open-Vocabulary Federated Learning with Multimodal PrototypingCode0
Continual Learning for Smart City: A Survey—0
Privacy-Aware Spectrum Pricing and Power Control Optimization for LEO Satellite Internet-of-Things—0
Supplementary File: Cooperative Gradient Coding for Semi-Decentralized Federated Learning—0
From Learning to Analytics: Improving Model Efficacy with Goal-Directed Client Selection—0
Computation and Communication Efficient Lightweighting Vertical Federated Learning for Smart Building IoT—0
Adaptive Decentralized Federated Learning in Energy and Latency Constrained Wireless Networks—0
Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning—0
Biased Over-the-Air Federated Learning under Wireless Heterogeneity—0
Client-supervised Federated Learning: Towards One-model-for-all Personalization—0
FRESCO: Federated Reinforcement Energy System for Cooperative Optimization—0
Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model UpdatesCode0
Generalized Policy Learning for Smart Grids: FL TRPO Approach—0
Spikewhisper: Temporal Spike Backdoor Attacks on Federated Neuromorphic Learning over Low-power Devices—0
CoRAST: Towards Foundation Model-Powered Correlated Data Analysis in Resource-Constrained CPS and IoT—0
Enhancing Privacy in Federated Learning through Local Training—0
Show:102550
← PrevPage 57 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