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 36513700 of 6771 papers

TitleStatusHype
LOKI: Large-scale Data Reconstruction Attack against Federated Learning through Model ManipulationCode0
A Survey on Class Imbalance in Federated Learning0
STDLens: Model Hijacking-Resilient Federated Learning for Object DetectionCode0
Poisoning Attacks in Federated Edge Learning for Digital Twin 6G-enabled IoTs: An Anticipatory Study0
FedMAE: Federated Self-Supervised Learning with One-Block Masked Auto-Encoder0
Recursive Euclidean Distance Based Robust Aggregation Technique For Federated Learning0
Make Landscape Flatter in Differentially Private Federated LearningCode1
FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning SystemCode4
On the Convergence of Decentralized Federated Learning Under Imperfect Information SharingCode0
A Survey of Federated Learning for Connected and Automated Vehicles0
Experimenting with Normalization Layers in Federated Learning on non-IID scenariosCode0
PFSL: Personalized & Fair Split Learning with Data & Label Privacy for thin clientsCode1
Hierarchical Personalized Federated Learning Over Massive Mobile Edge Computing Networks0
Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach0
Byzantine-Resilient Federated Learning at Edge0
FedRight: An Effective Model Copyright Protection for Federated Learning0
Report of the Medical Image De-Identification (MIDI) Task Group -- Best Practices and Recommendations0
No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed ClassifierCode1
An Empirical Evaluation of Federated Contextual Bandit Algorithms0
GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph DataCode0
Towards Cooperative Federated Learning over Heterogeneous Edge/Fog Networks0
Efficient and Secure Federated Learning for Financial Applications0
Optimization Design for Federated Learning in Heterogeneous 6G Networks0
Visual Prompt Based Personalized Federated Learning0
Comparative Evaluation of Data Decoupling Techniques for Federated Machine Learning with Database as a Service0
Model Extraction Attacks on Split Federated Learning0
Network Anomaly Detection Using Federated Learning0
Cross-device Federated Learning for Mobile Health Diagnostics: A First Study on COVID-19 Detection0
TARGET: Federated Class-Continual Learning via Exemplar-Free DistillationCode1
Multi-metrics adaptively identifies backdoors in Federated learningCode1
Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging0
Making Batch Normalization Great in Federated Deep Learning0
Stabilizing and Improving Federated Learning with Non-IID Data and Client Dropout0
FedLP: Layer-wise Pruning Mechanism for Communication-Computation Efficient Federated LearningCode1
Privacy-Preserving Cooperative Visible Light Positioning for Nonstationary Environment: A Federated Learning Perspective0
Zone-based Federated Learning for Mobile Sensing Data0
Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learning0
Complement Sparsification: Low-Overhead Model Pruning for Federated Learning0
Papaya: Federated Learning, but Fully Decentralized0
Optimizing Federated Learning for Medical Image Classification on Distributed Non-iid Datasets with Partial Labels0
An Evaluation of Non-Contrastive Self-Supervised Learning for Federated Medical Image Analysis0
FedREP: A Byzantine-Robust, Communication-Efficient and Privacy-Preserving Framework for Federated Learning0
Semi-Federated Learning for Collaborative Intelligence in Massive IoT NetworksCode1
Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clustering0
Memory-adaptive Depth-wise Heterogenous Federated LearningCode0
Model-Agnostic Federated LearningCode0
Considerations on the Theory of Training Models with Differential Privacy0
Privacy-preserving and Uncertainty-aware Federated Trajectory Prediction for Connected Autonomous Vehicles0
Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learning0
A Privacy Preserving System for Movie Recommendations Using Federated Learning0
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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