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 55015550 of 6771 papers

TitleStatusHype
Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networks0
FedSkel: Efficient Federated Learning on Heterogeneous Systems with Skeleton Gradients UpdateCode0
Cross-Silo Federated Learning for Multi-Tier Networks with Vertical and Horizontal Data Partitioning0
Towards More Efficient Federated Learning with Better Optimization Objects0
Order Optimal Bounds for One-Shot Federated Learning over non-Convex Loss Functions0
Multi-task Federated Learning for Heterogeneous Pancreas Segmentation0
Addressing Algorithmic Disparity and Performance Inconsistency in Federated LearningCode1
Multi-Center Federated Learning: Clients Clustering for Better PersonalizationCode1
EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated LearningCode1
Practical and Secure Federated Recommendation with Personalized Masks0
Learning Federated Representations and Recommendations with Limited Negatives0
Collaboration Equilibrium in Federated LearningCode1
Fed-TGAN: Federated Learning Framework for Synthesizing Tabular DataCode1
Federated Learning with Correlated Data: Taming the Tail for Age-Optimal Industrial IoT0
Federated Multi-Target Domain Adaptation0
Aggregation Delayed Federated LearningCode0
Client Selection Approach in Support of Clustered Federated Learning over Wireless Edge Networks0
Federated Asymptotics: a model to compare federated learning algorithms0
Aegis: A Trusted, Automatic and Accurate Verification Framework for Vertical Federated Learning0
FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learning0
A Novel Attribute Reconstruction Attack in Federated Learning0
Blockchain-based Trustworthy Federated Learning Architecture0
Federated Adversarial Debiasing for Fair and Transferable RepresentationsCode1
Collaborative Unsupervised Visual Representation Learning from Decentralized DataCode0
Joint Optimization in Edge-Cloud Continuum for Federated Unsupervised Person Re-identification0
FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated LearningCode2
Dynamic Attention-based Communication-Efficient Federated Learning0
A Contract Theory based Incentive Mechanism for Federated Learning0
An Operator Splitting View of Federated Learning0
Communication Optimization in Large Scale Federated Learning using Autoencoder Compressed Weight Updates0
FedMatch: Federated Learning Over Heterogeneous Question Answering DataCode1
ABC-FL: Anomalous and Benign client Classification in Federated Learning0
FedPAGE: A Fast Local Stochastic Gradient Method for Communication-Efficient Federated Learning0
FederatedNILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring based on Federated Deep Learning0
Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryption0
The Effect of Training Parameters and Mechanisms on Decentralized Federated Learning based on MNIST DatasetCode0
Fed-BEV: A Federated Learning Framework for Modelling Energy Consumption of Battery Electric Vehicles0
User Scheduling for Federated Learning Through Over-the-Air Computation0
GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learning0
On Addressing Heterogeneity in Federated Learning for Autonomous Vehicles Connected to a Drone Orchestrator0
Multi-task Federated Edge Learning (MtFEEL) in Wireless Networks0
Secure and Privacy-Preserving Federated Learning via Co-Utility0
FedJAX: Federated learning simulation with JAXCode1
Personalized Federated Learning with Clustering: Non-IID Heart Rate Variability Data Application0
Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challenges0
Information Stealing in Federated Learning Systems Based on Generative Adversarial Networks0
Communication-Efficient Federated Learning via Predictive CodingCode0
A Decentralized Federated Learning Framework via Committee Mechanism with Convergence GuaranteeCode1
Distributed Learning for Time-varying Networks: A Scalable Design0
Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness0
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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