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 751800 of 6771 papers

TitleStatusHype
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated LearningCode1
Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated LearningCode1
How to Combine Variational Bayesian Networks in Federated LearningCode1
Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy GuaranteesCode1
Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT ImagingCode1
IBM Federated Learning: an Enterprise Framework White Paper V0.1Code1
C2A: Client-Customized Adaptation for Parameter-Efficient Federated LearningCode1
Implicit Model Specialization through DAG-based Decentralized Federated LearningCode1
Improving Transferability of Network Intrusion Detection in a Federated Learning SetupCode1
FL-Market: Trading Private Models in Federated LearningCode1
Intrusion Detection with Segmented Federated Learning for Large-Scale Multiple LANsCode1
Inverting Gradients -- How easy is it to break privacy in federated learning?Code1
Joint Privacy Enhancement and Quantization in Federated LearningCode1
Knowledge-Aware Federated Active Learning with Non-IID DataCode1
Label Inference Attacks Against Vertical Federated LearningCode1
Label Leakage and Protection in Two-party Split LearningCode1
Analysis and Evaluation of Synchronous and Asynchronous FLchainCode1
Collaboration Equilibrium in Federated LearningCode1
Learning to Transmit with Provable Guarantees in Wireless Federated LearningCode1
Lightwave Power Transfer for Federated Learning-based Wireless NetworksCode1
Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware MinimizationCode1
Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated LearningCode1
Lockdown: Backdoor Defense for Federated Learning with Isolated Subspace TrainingCode1
LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID DatasetsCode1
Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated LearningCode1
ARFED: Attack-Resistant Federated averaging based on outlier eliminationCode1
CAR-MFL: Cross-Modal Augmentation by Retrieval for Multimodal Federated Learning with Missing ModalitiesCode1
Meta-HAR: Federated Representation Learning for Human Activity RecognitionCode1
CoDeC: Communication-Efficient Decentralized Continual LearningCode1
Mitigating Communications Threats in Decentralized Federated Learning through Moving Target DefenseCode1
Mitigating Unintended Memorization with LoRA in Federated Learning for LLMsCode1
mixEEG: Enhancing EEG Federated Learning for Cross-subject EEG Classification with Tailored mixupCode1
ALI-DPFL: Differentially Private Federated Learning with Adaptive Local IterationsCode1
Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT ImagingCode1
Modeling Global Distribution for Federated Learning with Label Distribution SkewCode1
Model-sharing Games: Analyzing Federated Learning Under Voluntary ParticipationCode1
A Better Alternative to Error Feedback for Communication-Efficient Distributed LearningCode1
Multi-Armed Bandit Based Client Scheduling for Federated LearningCode1
Multi-Center Federated Learning: Clients Clustering for Better PersonalizationCode1
Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated LearningCode1
Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE ResultsCode1
Multi-Stage Hybrid Federated Learning over Large-Scale D2D-Enabled Fog NetworksCode1
Neurotoxin: Durable Backdoors in Federated LearningCode1
Node Selection Toward Faster Convergence for Federated Learning on Non-IID DataCode1
Cross-Silo Prototypical Calibration for Federated Learning with Non-IID DataCode1
Bayesian Framework for Gradient LeakageCode1
Estimation of Continuous Blood Pressure from PPG via a Federated Learning ApproachCode1
OLIVE: Oblivious Federated Learning on Trusted Execution Environment against the risk of sparsificationCode1
Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated FeaturesCode1
Learning from History for Byzantine Robust OptimizationCode1
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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