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 150 of 6771 papers

TitleStatusHype
A Distributed Generative AI Approach for Heterogeneous Multi-Domain Environments under Data Sharing constraintsCode0
FedGA: A Fair Federated Learning Framework Based on the Gini Coefficient0
Federated Learning for Commercial Image Sources0
Federated Learning in Open- and Closed-Loop EMG Decoding: A Privacy and Performance Perspective0
A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy0
A Bayesian Incentive Mechanism for Poison-Resilient Federated Learning0
Safeguarding Federated Learning-based Road Condition Classification0
Self-Adaptive and Robust Federated Spectrum Sensing without Benign Majority for Cellular Networks0
Site-Level Fine-Tuning with Progressive Layer Freezing: Towards Robust Prediction of Bronchopulmonary Dysplasia from Day-1 Chest Radiographs in Extremely Preterm Infants0
ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs0
Sporadic Federated Learning Approach in Quantum Environment to Tackle Quantum Noise0
Quantized Rank Reduction: A Communications-Efficient Federated Learning Scheme for Network-Critical Applications0
D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data0
FLsim: A Modular and Library-Agnostic Simulation Framework for Federated LearningCode0
Feature Distillation is the Better Choice for Model-Heterogeneous Federated Learning0
Domain Borders Are There to Be Crossed With Federated Few-Shot AdaptationCode0
Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout0
MTF-Grasp: A Multi-tier Federated Learning Approach for Robotic Grasping0
Convergence of Agnostic Federated Averaging0
Privacy-Preserving Multi-Stage Fall Detection Framework with Semi-supervised Federated Learning and Robotic Vision Confirmation0
FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise0
Federated Learning with Graph-Based Aggregation for Traffic Forecasting0
Lightweight Federated Learning over Wireless Edge Networks0
DRAGD: A Federated Unlearning Data Reconstruction Attack Based on Gradient Differences0
Model Parallelism With Subnetwork Data Parallelism0
SFedKD: Sequential Federated Learning with Discrepancy-Aware Multi-Teacher Knowledge Distillation0
Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift0
Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry DetectionCode0
Sparse Self-Federated Learning for Energy Efficient Cooperative Intelligence in Society 5.00
HLF-FSL. A Decentralized Federated Split Learning Solution for IoT on Hyperledger Fabric0
Quantum Federated Learning for Multimodal Data: A Modality-Agnostic Approach0
Efficient Federated Learning with Timely Update Dissemination0
Prototype-Guided and Lightweight Adapters for Inherent Interpretation and Generalisation in Federated LearningCode0
Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach0
Communication-Efficient Module-Wise Federated Learning for Grasp Pose Detection in Cluttered Environments0
A Federated Learning-based Lightweight Network with Zero Trust for UAV Authentication0
FedPall: Prototype-based Adversarial and Collaborative Learning for Federated Learning with Feature DriftCode0
BackFed: An Efficient & Standardized Benchmark Suite for Backdoor Attacks in Federated LearningCode2
Kalman Filter Aided Federated Koopman Learning0
pFedMMA: Personalized Federated Fine-Tuning with Multi-Modal Adapter for Vision-Language ModelsCode0
TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained EnvironmentsCode0
Heterogeneous Federated Learning with Prototype Alignment and UpscalingCode0
Communication Efficient, Differentially Private Distributed Optimization using Correlation-Aware Sketching0
S2FGL: Spatial Spectral Federated Graph LearningCode0
Embedding-Based Federated Data Sharing via Differentially Private Conditional VAEsCode0
Fluid Democracy in Federated Data Aggregation0
Federated Learning for ICD Classification with Lightweight Models and Pretrained Embeddings0
VeFIA: An Efficient Inference Auditing Framework for Vertical Federated Collaborative Software0
A Privacy-Preserving Indoor Localization System based on Hierarchical Federated Learning0
FedRef: Communication-Efficient Bayesian Fine Tuning with Reference ModelCode0
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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