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 47014750 of 6771 papers

TitleStatusHype
Recovering Private Text in Federated Learning of Language ModelsCode1
Massive MIMO for Serving Federated Learning and Non-Federated Learning Users0
Serving Federated Learning and Non-Federated Learning Users: A Massive MIMO Approach0
On the (In)security of Peer-to-Peer Decentralized Machine LearningCode0
Providing Location Information at Edge Networks: A Federated Learning-Based Approach0
Federated Anomaly Detection over Distributed Data Streams0
FedHAP: Fast Federated Learning for LEO Constellations Using Collaborative HAPs0
Federated Learning Under Intermittent Client Availability and Time-Varying Communication ConstraintsCode1
Tighter Regret Analysis and Optimization of Online Federated Learning0
Secure Aggregation for Federated Learning in Flower0
Over-the-Air Federated Learning with Joint Adaptive Computation and Power Control0
Secure & Private Federated NeuroimagingCode2
eFedDNN: Ensemble based Federated Deep Neural Networks for Trajectory Mode Inference0
Blockchain-based Secure Client Selection in Federated Learning0
A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural NetworksCode0
Deep Federated Anomaly Detection for Multivariate Time Series Data0
Residue-based Label Protection Mechanisms in Vertical Logistic Regression0
LSTM-Based Distributed Conditional Generative Adversarial Network For Data-Driven 5G-Enabled Maritime UAV Communications0
Federated Multi-Armed Bandits Under Byzantine Attacks0
Protecting Data from all Parties: Combining FHE and DP in Federated Learning0
ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated LearningCode1
Federated Random Reshuffling with Compression and Variance Reduction0
Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review0
Network Gradient Descent Algorithm for Decentralized Federated Learning0
Online Model Compression for Federated Learning with Large Models0
Federated Learning with Noisy User Feedback0
Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-Ray DataCode0
Federated Channel Learning for Intelligent Reflecting Surfaces With Fewer Pilot Signals0
Intelligent Transportation Systems' Orchestration: Lessons Learned & Potential Opportunities0
Can collaborative learning be private, robust and scalable?0
Over-The-Air Federated Learning under Byzantine Attacks0
Uncertainty Minimization for Personalized Federated Semi-Supervised Learning0
Communication-Efficient Adaptive Federated LearningCode1
FedSPLIT: One-Shot Federated Recommendation System Based on Non-negative Joint Matrix Factorization and Knowledge Distillation0
FedMix: Mixed Supervised Federated Learning for Medical Image SegmentationCode1
FedGiA: An Efficient Hybrid Algorithm for Federated LearningCode1
MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning0
Revisiting Communication-Efficient Federated Learning with Balanced Global and Local Updates0
Local Stochastic Bilevel Optimization with Momentum-Based Variance Reduction0
FedRN: Exploiting k-Reliable Neighbors Towards Robust Federated LearningCode0
Training Mixed-Domain Translation Models via Federated Learning0
Privacy Amplification via Random Participation in Federated Learning0
Performance Weighting for Robust Federated Learning Against Corrupted Sources0
FedDKD: Federated Learning with Decentralized Knowledge Distillation0
ActPerFL: Active Personalized Federated Learning0
Intrinsic Gradient Compression for Scalable and Efficient Federated Learning0
A New Dimensionality Reduction Method Based on Hensel's Compression for Privacy Protection in Federated Learning0
Reward Systems for Trustworthy Medical Federated LearningCode0
FEDIC: Federated Learning on Non-IID and Long-Tailed Data via Calibrated DistillationCode1
Bridging Differential Privacy and Byzantine-Robustness via Model AggregationCode0
Show:102550
← PrevPage 95 of 136Next →

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