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

Showing 21012150 of 6771 papers

TitleStatusHype
An Autoencoder-Based Constellation Design for AirComp in Wireless Federated Learning0
SpamDam: Towards Privacy-Preserving and Adversary-Resistant SMS Spam DetectionCode0
On the Efficiency of Privacy Attacks in Federated LearningCode0
VFLGAN: Vertical Federated Learning-based Generative Adversarial Network for Vertically Partitioned Data PublicationCode0
MAP: Model Aggregation and Personalization in Federated Learning with Incomplete Classes0
Bridging Data Islands: Geographic Heterogeneity-Aware Federated Learning for Collaborative Remote Sensing Semantic Segmentation0
FedDistill: Global Model Distillation for Local Model De-Biasing in Non-IID Federated Learning0
FedCCL: Federated Dual-Clustered Feature Contrast Under Domain Heterogeneity0
PraFFL: A Preference-Aware Scheme in Fair Federated LearningCode0
FLoRA: Enhancing Vision-Language Models with Parameter-Efficient Federated LearningCode0
Federated Optimization with Doubly Regularized Drift Correction0
Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing0
FedAuxHMTL: Federated Auxiliary Hard-Parameter Sharing Multi-Task Learning for Network Edge Traffic Classification0
Bayesian Federated Model Compression for Communication and Computation Efficiency0
Improving Multi-Center Generalizability of GAN-Based Fat Suppression using Federated Learning0
The Sandwich meta-framework for architecture agnostic deep privacy-preserving transfer learning for non-invasive brainwave decoding0
Logit Calibration and Feature Contrast for Robust Federated Learning on Non-IID Data0
Federated learning model for predicting major postoperative complications0
Collaborative Multi-source Domain Adaptation Through Optimal Transport0
Dynamic D2D-Assisted Federated Learning over O-RAN: Performance Analysis, MAC Scheduler, and Asymmetric User Selection0
Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models0
Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid Byzantines in Federated LearningCode0
FLEX: FLEXible Federated Learning FrameworkCode4
Open-Source AI-based SE Tools: Opportunities and Challenges of Collaborative Software Learning0
pfl-research: simulation framework for accelerating research in Private Federated LearningCode3
SoK: On Gradient Leakage in Federated Learning0
PlateSegFL: A Privacy-Preserving License Plate Detection Using Federated Segmentation Learning0
Hyperparameter Optimization for SecureBoost via Constrained Multi-Objective Federated Learning0
Vanishing Variance Problem in Fully Decentralized Neural-Network Systems0
Prompt Public Large Language Models to Synthesize Data for Private On-device Applications0
Distributionally Robust Alignment for Medical Federated Vision-Language Pre-training Under Data Heterogeneity0
Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning0
Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach0
Approximate Gradient Coding for Privacy-Flexible Federated Learning with Non-IID DataCode0
Exploring Lightweight Federated Learning for Distributed Load Forecasting0
An Interpretable Client Decision Tree Aggregation process for Federated LearningCode0
FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-TuningCode1
Robust Federated Learning for Wireless Networks: A Demonstration with Channel Estimation0
Federated Computing -- Survey on Building Blocks, Extensions and Systems0
QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detection0
Optimal Batch Allocation for Wireless Federated Learning0
Federated Distillation: A Survey0
Federated Multi-Agent Mapping for Planetary Exploration0
Privacy-Aware Spectrum Pricing and Power Control Optimization for LEO Satellite Internet-of-Things0
Addressing Heterogeneity in Federated Load Forecasting with Personalization Layers0
Open-Vocabulary Federated Learning with Multimodal PrototypingCode0
Continual Learning for Smart City: A Survey0
Supplementary File: Cooperative Gradient Coding for Semi-Decentralized Federated Learning0
Computation and Communication Efficient Lightweighting Vertical Federated Learning for Smart Building IoT0
From Learning to Analytics: Improving Model Efficacy with Goal-Directed Client Selection0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SiloBN + ASAMmIoU49.75Unverified
2SiloBN + SAMmIoU49.1Unverified
3SiloBNmIoU45.96Unverified
4FedSAM + SWAmIoU43.42Unverified
5FedASAM + SWAmIoU43.02Unverified
6FedAvg + SWAmIoU42.48Unverified
7FedASAMmIoU42.27Unverified
8FedSAMmIoU41.22Unverified
9FedAvgmIoU38.65Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAAcc@1-1262Clients68.32Unverified
2FedSAM + SWAAcc@1-1262Clients68.12Unverified
3FedAvg + SWAAcc@1-1262Clients67.52Unverified
4FedASAMAcc@1-1262Clients64.23Unverified
5FedSAMAcc@1-1262Clients63.72Unverified
6FedAvgAcc@1-1262Clients61.91Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.64Unverified
2FedASAMACC@1-100Clients39.76Unverified
3FedSAM + SWAACC@1-100Clients39.51Unverified
4FedSAMACC@1-100Clients36.93Unverified
5FedAvgACC@1-100Clients36.74Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients41.62Unverified
2FedASAMACC@1-100Clients40.81Unverified
3FedSAM + SWAACC@1-100Clients39.24Unverified
4FedAvgACC@1-100Clients38.59Unverified
5FedSAMACC@1-100Clients38.56Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.72Unverified
2FedSAM + SWAACC@1-100Clients46.76Unverified
3FedASAMACC@1-100Clients46.58Unverified
4FedSAMACC@1-100Clients44.84Unverified
5FedAvgACC@1-100Clients41.27Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.27Unverified
2FedASAMACC@1-100Clients47.78Unverified
3FedSAM + SWAACC@1-100Clients46.47Unverified
4FedSAMACC@1-100Clients46.05Unverified
5FedAvgACC@1-100Clients42.17Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients49.17Unverified
2FedSAM + SWAACC@1-100Clients47.96Unverified
3FedASAMACC@1-100Clients45.61Unverified
4FedSAMACC@1-100Clients44.73Unverified
5FedAvgACC@1-100Clients40.43Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.01Unverified
2FedSAM + SWAACC@1-100Clients39.3Unverified
3FedASAMACC@1-100Clients36.04Unverified
4FedSAMACC@1-100Clients31.04Unverified
5FedAvgACC@1-100Clients30.25Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.97Unverified
2FedASAM + SWAACC@1-100Clients54.79Unverified
3FedSAM + SWAACC@1-100Clients53.67Unverified
4FedSAMACC@1-100Clients53.39Unverified
5FedAvgACC@1-100Clients50.25Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.5Unverified
2FedSAM + SWAACC@1-100Clients54.36Unverified
3FedASAM + SWAACC@1-100Clients54.1Unverified
4FedSAMACC@1-100Clients53.97Unverified
5FedAvgACC@1-100Clients50.66Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.81Unverified
2FedSAMACC@1-100Clients54.01Unverified
3FedSAM + SWAACC@1-100Clients53.9Unverified
4FedASAM + SWAACC@1-100Clients53.86Unverified
5FedAvgACC@1-100Clients49.92Unverified
#ModelMetricClaimedVerifiedStatus
1AdaBestAverage Top-1 Accuracy56.2Unverified