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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2051–2100 of 6771 papers

TitleStatusHype
Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition—0
Efficient Federated Learning with Enhanced Privacy via Lottery Ticket Pruning in Edge Computing—0
Byzantine-Resilient Over-the-Air Federated Learning under Zero-Trust Architecture—0
Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices—0
Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout—0
Byzantine-Resilient High-Dimensional Federated Learning—0
A new framework for prognostics in decentralized industries: Enhancing fairness, security, and transparency through Blockchain and Federated Learning—0
A Differentially Private Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations—0
A Closer Look at Personalization in Federated Image Classification—0
PA-CFL: Privacy-Adaptive Clustered Federated Learning for Transformer-Based Sales Forecasting on Heterogeneous Retail Data—0
Efficient Federated Learning via Local Adaptive Amended Optimizer with Linear Speedup—0
Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data—0
Byzantine-resilient Federated Learning With Adaptivity to Data Heterogeneity—0
Efficient Federated Learning over Multiple Access Channel with Differential Privacy Constraints—0
Byzantine-Resilient Federated Learning via Distributed Optimization—0
A New Dimensionality Reduction Method Based on Hensel's Compression for Privacy Protection in Federated Learning—0
Efficient Federated Learning for AIoT Applications Using Knowledge Distillation—0
Byzantine-Resilient Federated Learning at Edge—0
Byzantine-resilient Federated Learning Employing Normalized Gradients on Non-IID Datasets—0
An Evaluation of Non-Contrastive Self-Supervised Learning for Federated Medical Image Analysis—0
Efficient Federated Finetuning of Tiny Transformers with Resource-Constrained Devices—0
Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates—0
Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors—0
Efficient Device Scheduling with Multi-Job Federated Learning—0
Building Gradient Bridges: Label Leakage from Restricted Gradient Sharing in Federated Learning—0
Efficient Deployment of Large Language Models on Resource-constrained Devices—0
Efficient Data Valuation Approximation in Federated Learning: A Sampling-based Approach—0
Buffer is All You Need: Defending Federated Learning against Backdoor Attacks under Non-iids via Buffering—0
Efficient Data Distribution Estimation for Accelerated Federated Learning—0
Efficient Cross-Domain Federated Learning by MixStyle Approximation—0
Buffered Asynchronous Secure Aggregation for Cross-Device Federated Learning—0
On the Convergence of Federated Averaging under Partial Participation for Over-parameterized Neural Networks—0
Collaborative Distributed Machine Learning—0
Efficient Core-selecting Incentive Mechanism for Data Sharing in Federated Learning—0
Efficient Conformal Prediction under Data Heterogeneity—0
Efficient Collaborations through Weight-Driven Coalition Dynamics in Federated Learning Systems—0
Efficient Cluster Selection for Personalized Federated Learning: A Multi-Armed Bandit Approach—0
Budgeted Online Selection of Candidate IoT Clients to Participate in Federated Learning—0
An Enhanced Federated Prototype Learning Method under Domain Shift—0
Efficient Client Selection in Federated Learning—0
Efficient Client Contribution Evaluation for Horizontal Federated Learning—0
Budgeted Online Model Selection and Fine-Tuning via Federated Learning—0
Efficient Classification of SARS-CoV-2 Spike Sequences Using Federated Learning—0
Efficient Batch Homomorphic Encryption for Vertically Federated XGBoost—0
An Energy Optimized Specializing DAG Federated Learning based on Event Triggered Communication—0
Decision Models for Selecting Federated Learning Architecture Patterns—0
Efficient Asynchronous Federated Learning with Sparsification and Quantization—0
Efficient and Secure Federated Learning for Financial Applications—0
Distributed Non-Convex Optimization with One-Bit Compressors on Heterogeneous Data: Efficient and Resilient Algorithms—0
Efficient and Reliable Overlay Networks for Decentralized Federated Learning—0
Show:102550
← PrevPage 42 of 136Next →
All datasetsCityscapes heterogeneousLandmarks-User-160kCIFAR-100 (alpha=0, 10 clients per round)CIFAR-100 (alpha=0, 20 clients per round)CIFAR-100 (alpha=0.5, 10 clients per round)CIFAR-100 (alpha=0.5, 20 clients per round)CIFAR-100 (alpha=0.5, 5 clients per round)CIFAR-100 (alpha=0, 5 clients per round)CIFAR-100 (alpha=1000, 10 clients per round)CIFAR-100 (alpha=1000, 20 clients per round)CIFAR-100 (alpha=1000, 5 clients per round)CIFAR100 (alpha=0.3, 10 clients per round)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SiloBN + ASAMmIoU49.75—Unverified
2SiloBN + SAMmIoU49.1—Unverified
3SiloBNmIoU45.96—Unverified
4FedSAM + SWAmIoU43.42—Unverified
5FedASAM + SWAmIoU43.02—Unverified
6FedAvg + SWAmIoU42.48—Unverified
7FedASAMmIoU42.27—Unverified
8FedSAMmIoU41.22—Unverified
9FedAvgmIoU38.65—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAAcc@1-1262Clients68.32—Unverified
2FedSAM + SWAAcc@1-1262Clients68.12—Unverified
3FedAvg + SWAAcc@1-1262Clients67.52—Unverified
4FedASAMAcc@1-1262Clients64.23—Unverified
5FedSAMAcc@1-1262Clients63.72—Unverified
6FedAvgAcc@1-1262Clients61.91—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.64—Unverified
2FedASAMACC@1-100Clients39.76—Unverified
3FedSAM + SWAACC@1-100Clients39.51—Unverified
4FedSAMACC@1-100Clients36.93—Unverified
5FedAvgACC@1-100Clients36.74—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients41.62—Unverified
2FedASAMACC@1-100Clients40.81—Unverified
3FedSAM + SWAACC@1-100Clients39.24—Unverified
4FedAvgACC@1-100Clients38.59—Unverified
5FedSAMACC@1-100Clients38.56—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.72—Unverified
2FedSAM + SWAACC@1-100Clients46.76—Unverified
3FedASAMACC@1-100Clients46.58—Unverified
4FedSAMACC@1-100Clients44.84—Unverified
5FedAvgACC@1-100Clients41.27—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.27—Unverified
2FedASAMACC@1-100Clients47.78—Unverified
3FedSAM + SWAACC@1-100Clients46.47—Unverified
4FedSAMACC@1-100Clients46.05—Unverified
5FedAvgACC@1-100Clients42.17—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients49.17—Unverified
2FedSAM + SWAACC@1-100Clients47.96—Unverified
3FedASAMACC@1-100Clients45.61—Unverified
4FedSAMACC@1-100Clients44.73—Unverified
5FedAvgACC@1-100Clients40.43—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.01—Unverified
2FedSAM + SWAACC@1-100Clients39.3—Unverified
3FedASAMACC@1-100Clients36.04—Unverified
4FedSAMACC@1-100Clients31.04—Unverified
5FedAvgACC@1-100Clients30.25—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.97—Unverified
2FedASAM + SWAACC@1-100Clients54.79—Unverified
3FedSAM + SWAACC@1-100Clients53.67—Unverified
4FedSAMACC@1-100Clients53.39—Unverified
5FedAvgACC@1-100Clients50.25—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.5—Unverified
2FedSAM + SWAACC@1-100Clients54.36—Unverified
3FedASAM + SWAACC@1-100Clients54.1—Unverified
4FedSAMACC@1-100Clients53.97—Unverified
5FedAvgACC@1-100Clients50.66—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.81—Unverified
2FedSAMACC@1-100Clients54.01—Unverified
3FedSAM + SWAACC@1-100Clients53.9—Unverified
4FedASAM + SWAACC@1-100Clients53.86—Unverified
5FedAvgACC@1-100Clients49.92—Unverified
#ModelMetricClaimedVerifiedStatus
1AdaBestAverage Top-1 Accuracy56.2—Unverified