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 451500 of 6771 papers

TitleStatusHype
An Empirical Study of Personalized Federated LearningCode1
FedMAX: Mitigating Activation Divergence for Accurate and Communication-Efficient Federated LearningCode1
FedMed-GAN: Federated Domain Translation on Unsupervised Cross-Modality Brain Image SynthesisCode1
FedMedICL: Towards Holistic Evaluation of Distribution Shifts in Federated Medical ImagingCode1
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of ModelsCode1
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine LearningCode1
Bayesian Framework for Gradient LeakageCode1
Benchmarking Algorithms for Federated Domain GeneralizationCode1
FedMSE: Federated learning for IoT network intrusion detectionCode1
Fed-MUnet: Multi-modal Federated Unet for Brain Tumor SegmentationCode1
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated LearningCode1
FedNoisy: Federated Noisy Label Learning BenchmarkCode1
Backdoor Attacks on Federated Learning with Lottery Ticket HypothesisCode1
FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated LearningCode1
FedPop: Federated Population-based Hyperparameter TuningCode1
FedProc: Prototypical Contrastive Federated Learning on Non-IID dataCode1
FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous ClientsCode1
FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image ClassificationCode1
FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model ExtractionCode1
FedScale: Benchmarking Model and System Performance of Federated Learning at ScaleCode1
FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated LearningCode1
FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model InterpolationCode1
FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated LearningCode1
Fedstellar: A Platform for Decentralized Federated LearningCode1
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated LearningCode1
Fed-TGAN: Federated Learning Framework for Synthesizing Tabular DataCode1
FedTP: Federated Learning by Transformer PersonalizationCode1
A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian RegularizationCode1
A Distributed Trust Framework for Privacy-Preserving Machine LearningCode1
FeSViBS: Federated Split Learning of Vision Transformer with Block SamplingCode1
FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel ExtractionCode1
Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated LearningCode1
Auditing Privacy Defenses in Federated Learning via Generative Gradient LeakageCode1
Attribute Inference Attack of Speech Emotion Recognition in Federated Learning SettingsCode1
FLASHE: Additively Symmetric Homomorphic Encryption for Cross-Silo Federated LearningCode1
FLASH-RL: Federated Learning Addressing System and Static Heterogeneity using Reinforcement LearningCode1
FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated LearningCode1
FLIP: Towards Comprehensive and Reliable Evaluation of Federated Prompt LearningCode1
FedAdapter: Efficient Federated Learning for Modern NLPCode1
Flower: A Friendly Federated Learning Research FrameworkCode1
FLTrust: Byzantine-robust Federated Learning via Trust BootstrappingCode1
ARFED: Attack-Resistant Federated averaging based on outlier eliminationCode1
FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client PerspectiveCode1
Anomaly-Flow: A Multi-domain Federated Generative Adversarial Network for Distributed Denial-of-Service DetectionCode1
FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model FusionCode1
Byzantine-Robust Learning on Heterogeneous Data via Gradient SplittingCode1
Generative Autoregressive Transformers for Model-Agnostic Federated MRI ReconstructionCode1
Generative Models for Effective ML on Private, Decentralized DatasetsCode1
Beyond Traditional Threats: A Persistent Backdoor Attack on Federated LearningCode1
Asynchronous Federated Learning for Edge-assisted Vehicular NetworksCode1
Show:102550
← PrevPage 10 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