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 64516500 of 6771 papers

TitleStatusHype
A Stochastic Optimization Framework for Private and Fair Learning From Decentralized DataCode0
PPS-QMIX: Periodically Parameter Sharing for Accelerating Convergence of Multi-Agent Reinforcement LearningCode0
BAFFLE : Blockchain Based Aggregator Free Federated LearningCode0
Federated Frank-Wolfe AlgorithmCode0
Efficient Federated Intrusion Detection in 5G ecosystem using optimized BERT-based modelCode0
Feature Distribution Matching for Federated Domain GeneralizationCode0
Real World Federated Learning with a Knowledge Distilled Transformer for Cardiac CT ImagingCode0
Federated Few-shot Learning for Cough Classification with Edge DevicesCode0
An Accuracy-Lossless Perturbation Method for Defending Privacy Attacks in Federated LearningCode0
Efficient and Robust Regularized Federated RecommendationCode0
Replica Tree-based Federated Learning using Limited DataCode0
GTFLAT: Game Theory Based Add-On For Empowering Federated Learning Aggregation TechniquesCode0
GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated LearningCode0
Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal PerspectivesCode0
Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed BanditCode0
Backdoor Attack is a Devil in Federated GAN-based Medical Image SynthesisCode0
A Whole-Process Certifiably Robust Aggregation Method Against Backdoor Attacks in Federated LearningCode0
Handling Data Heterogeneity in Federated Learning via Knowledge Distillation and FusionCode0
A Wasserstein Minimax Framework for Mixed Linear RegressionCode0
Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed BanditCode0
Practical Differentially Private and Byzantine-resilient Federated LearningCode0
Federated f-Differential PrivacyCode0
Federated Fairness Analytics: Quantifying Fairness in Federated LearningCode0
Congruent Learning for Self-Regulated Federated Learning in 6GCode0
Towards Optimal Customized Architecture for Heterogeneous Federated Learning with Contrastive Cloud-Edge Model DecouplingCode0
Harnessing the Power of Federated Learning in Federated Contextual BanditsCode0
TinyML NLP Scheme for Semantic Wireless Sentiment Classification with Privacy PreservationCode0
Semi-Supervised Federated Learning for Keyword SpottingCode0
TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained EnvironmentsCode0
Adaptive Federated Learning with Auto-Tuned ClientsCode0
HCFL: A High Compression Approach for Communication-Efficient Federated Learning in Very Large Scale IoT NetworksCode0
MyDigiTwin: A Privacy-Preserving Framework for Personalized Cardiovascular Risk Prediction and Scenario ExplorationCode0
Achieving Model Fairness in Vertical Federated LearningCode0
Continual Adaptation of Vision Transformers for Federated LearningCode0
Federated Face Forgery Detection Learning with Personalized RepresentationCode0
Effectiveness of Federated Learning and CNN Ensemble Architectures for Identifying Brain Tumors Using MRI ImagesCode0
Comparative Evaluation of Clustered Federated Learning MethodsCode0
PraFFL: A Preference-Aware Scheme in Fair Federated LearningCode0
Semi-Targeted Model Poisoning Attack on Federated Learning via Backward Error AnalysisCode0
TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy ClientsCode0
Accelerating Federated Learning with a Global Biased OptimiserCode0
Principled Federated Domain Adaptation: Gradient Projection and Auto-WeightingCode0
Client Adaptation improves Federated Learning with Simulated Non-IID ClientsCode0
Towards Practical Federated Causal Structure LearningCode0
Heterogeneous Datasets for Federated Survival Analysis SimulationCode0
Near-Optimal Collaborative Learning in BanditsCode0
Heterogeneous federated collaborative filtering using FAIR: Federated Averaging in Random SubspacesCode0
Predicting Infant Brain Connectivity with Federated Multi-Trajectory GNNs using Scarce DataCode0
A V2X-based Privacy Preserving Federated Measuring and Learning SystemCode0
A dual approach for federated learningCode0
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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