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 48514900 of 6771 papers

TitleStatusHype
FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning SimulationsCode1
Sparse Federated Learning with Hierarchical Personalized Models0
ST-FL: Style Transfer Preprocessing in Federated Learning for COVID-19 Segmentation0
Addressing Client Drift in Federated Continual Learning with Adaptive Optimization0
SwiftAgg+: Achieving Asymptotically Optimal Communication Loads in Secure Aggregation for Federated Learning0
FedGradNorm: Personalized Federated Gradient-Normalized Multi-Task Learning0
A Two-Stage Federated Transfer Learning Framework in Medical Images Classification on Limited Data: A COVID-19 Case Study0
Efficient Fully Distributed Federated Learning with Adaptive Local Links0
Asynchronous Collaborative Learning Across Data Silos0
Contextual Model Aggregation for Fast and Robust Federated Learning in Edge Computing0
Towards Effective Clustered Federated Learning: A Peer-to-peer Framework with Adaptive Neighbor Matching0
Federated Self-Supervised Learning for Acoustic Event Classification0
FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and CorrectionCode1
Semi-Targeted Model Poisoning Attack on Federated Learning via Backward Error AnalysisCode0
Federated Class-Incremental LearningCode1
Improving Generalization in Federated Learning by Seeking Flat MinimaCode1
Feature Distribution Matching for Federated Domain GeneralizationCode0
Interpretability of Fine-grained Classification of Sadness and DepressionCode0
Federated Spatial Reuse Optimization in Next-Generation Decentralized IEEE 802.11 WLANs0
Federated Learning Approach for Lifetime Prediction of Semiconductor Lasers0
Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithm0
Fair Federated Learning via Bounded Group Loss0
Efficient Split-Mix Federated Learning for On-Demand and In-Situ CustomizationCode1
Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey0
Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentation0
Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computing0
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding AggregationCode1
Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated LearningCode1
MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clients0
Training a Tokenizer for Free with Private Federated Learning0
Semi-FedSER: Semi-supervised Learning for Speech Emotion Recognition On Federated Learning using Multiview Pseudo-LabelingCode1
SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge IntelligenceCode1
Privacy-Aware Compression for Federated Data AnalysisCode1
Energy-Latency Attacks via Sponge PoisoningCode1
Communication-Efficient Federated Distillation with Active Data Sampling0
The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining0
Privatized Graph Federated Learning0
Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases0
Private Non-Convex Federated Learning Without a Trusted ServerCode0
Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation0
No Free Lunch Theorem for Security and Utility in Federated Learning0
Wireless Quantized Federated Learning: A Joint Computation and Communication Design0
FedSyn: Synthetic Data Generation using Federated Learning0
Federated Remote Physiological Measurement with Imperfect Data0
A Systematic Literature Review on Blockchain Enabled Federated Learning Framework for Internet of Vehicles0
CoCoFL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and QuantizationCode1
A Contribution-based Device Selection Scheme in Federated Learning0
Federated Minimax Optimization: Improved Convergence Analyses and Algorithms0
Robust Federated Learning Against Adversarial Attacks for Speech Emotion Recognition0
Update Compression for Deep Neural Networks on the Edge0
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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