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 64016450 of 6771 papers

TitleStatusHype
Attacks to Federated Learning: Responsive Web User Interface to Recover Training Data from User Gradients0
From Federated to Fog Learning: Distributed Machine Learning over Heterogeneous Wireless Networks0
An Efficient Framework for Clustered Federated LearningCode1
UVeQFed: Universal Vector Quantization for Federated LearningCode1
LDP-Fed: Federated Learning with Local Differential Privacy0
Federated Learning for 6G Communications: Challenges, Methods, and Future Directions0
A Distributed Trust Framework for Privacy-Preserving Machine LearningCode1
Wireless Communications for Collaborative Federated Learning0
Secure Sum Outperforms Homomorphic Encryption in (Current) Collaborative Deep Learning0
Federated Learning in Vehicular Networks0
Federated Face Presentation Attack Detection0
Continual Local Training for Better Initialization of Federated ModelsCode1
Towards Efficient Scheduling of Federated Mobile Devices under Computational and Statistical Heterogeneity0
Reliability and Performance Assessment of Federated Learning on Clinical Benchmark Data0
MVStylizer: An Efficient Edge-Assisted Video Photorealistic Style Transfer System for Mobile Phones0
FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID DataCode1
Global Multiclass Classification and Dataset Construction via Heterogeneous Local Experts0
Training Keyword Spotting Models on Non-IID Data with Federated Learning0
Federated Learning for Hybrid Beamforming in mm-Wave Massive MIMO0
Large-Scale Secure XGB for Vertical Federated Learning0
Efficient Federated Learning over Multiple Access Channel with Differential Privacy Constraints0
Federated Recommendation System via Differential Privacy0
Industrial Federated Learning -- Requirements and System Design0
A Secure Federated Learning Framework for 5G Networks0
Pretraining Federated Text Models for Next Word PredictionCode1
FedSplit: An algorithmic framework for fast federated optimization0
Intracranial Hemorrhage Detection Using Neural Network Based Methods With Federated Learning0
Efficient Privacy Preserving Edge Computing Framework for Image Classification0
Cloud-based Federated Boosting for Mobile Crowdsensing0
A Federated Learning Framework for Healthcare IoT devices0
Federated Generative Adversarial Learning0
Information-Theoretic Bounds on the Generalization Error and Privacy Leakage in Federated Learning0
Multi-Center Federated Learning: Clients Clustering for Better PersonalizationCode1
DBA: Distributed Backdoor Attacks against Federated LearningCode1
Differentially Private Federated Learning with Laplacian Smoothing0
Private Dataset Generation Using Privacy Preserving Collaborative LearningCode0
Omnidirectional Transfer for Quasilinear Lifelong LearningCode1
Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated LearningCode1
Towards Ubiquitous AI in 6G with Federated Learning0
Federated Transfer Learning for EEG Signal ClassificationCode1
SplitFed: When Federated Learning Meets Split LearningCode1
Federated learning with hierarchical clustering of local updates to improve training on non-IID data0
A Review of Privacy-preserving Federated Learning for the Internet-of-Things0
StochaLM: a Stochastic alternate Linearization Method for distributed optimization0
Federated Stochastic Gradient Langevin DynamicsCode0
A Framework for Evaluating Gradient Leakage Attacks in Federated Learning0
Hierarchically Fair Federated Learning0
Federated Learning with Only Positive LabelsCode0
Lottery Hypothesis based Unsupervised Pre-training for Model Compression in Federated Learning0
Data Poisoning Attacks on Federated Machine 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