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

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Showing 1801–1850 of 6771 papers

TitleStatusHype
Controlling Participation in Federated Learning with FeedbackCode0
Streamlined Federated Unlearning: Unite as One to Be Highly Efficient—0
Task Arithmetic Through The Lens Of One-Shot Federated Learning—0
Federated Learning with Uncertainty and Personalization via Efficient Second-order Optimization—0
Locally Differentially Private Online Federated Learning With Correlated Noise—0
Hidden Data Privacy Breaches in Federated Learning—0
Distributed Sign Momentum with Local Steps for Training TransformersCode0
A High Energy-Efficiency Multi-core Neuromorphic Architecture for Deep SNN Training—0
Adaptive Client Selection with Personalization for Communication Efficient Federated LearningCode0
An Empirical Study of Vulnerability Detection using Federated Learning—0
TIFeD: a Tiny Integer-based Federated learning algorithm with Direct feedback alignmentCode0
Understanding Generalization of Federated Learning: the Trade-off between Model Stability and Optimization—0
Distributed Online Optimization with Stochastic Agent Availability—0
Distributed, communication-efficient, and differentially private estimation of KL divergence—0
BadSFL: Backdoor Attack against Scaffold Federated Learning—0
Towards Efficient Model-Heterogeneity Federated Learning for Large Models—0
Privacy-Preserving Federated Foundation Model for Generalist Ultrasound Artificial Intelligence—0
eFedLLM: Efficient LLM Inference Based on Federated Learning—0
Modality Alignment Meets Federated Broadcasting—0
Tackling Data Heterogeneity in Federated Time Series Forecasting—0
FedQP: Towards Accurate Federated Learning using Quadratic Programming Guided Mutation—0
Federated Learning in Chemical Engineering: A Tutorial on a Framework for Privacy-Preserving Collaboration Across Distributed Data SourcesCode0
Federated PCA and Estimation for Spiked Covariance Matrices: Optimal Rates and Efficient Algorithm—0
Partial Knowledge Distillation for Alleviating the Inherent Inter-Class Discrepancy in Federated Learning—0
Geminio: Language-Guided Gradient Inversion Attacks in Federated LearningCode0
LoRA-FAIR: Federated LoRA Fine-Tuning with Aggregation and Initialization Refinement—0
REFOL: Resource-Efficient Federated Online Learning for Traffic Flow ForecastingCode0
Split Federated Learning Over Heterogeneous Edge Devices: Algorithm and Optimization—0
FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous VehiclesCode0
Memory Backdoor Attacks on Neural Networks—0
On-device Content-based Recommendation with Single-shot Embedding Pruning: A Cooperative Game PerspectiveCode0
NCAirFL: CSI-Free Over-the-Air Federated Learning Based on Non-Coherent Detection—0
DeTrigger: A Gradient-Centric Approach to Backdoor Attack Mitigation in Federated Learning—0
Attribute Inference Attacks for Federated Regression TasksCode0
Hyper-parameter Optimization for Federated Learning with Step-wise Adaptive Mechanism—0
Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions—0
FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for Federated Learning—0
Federated Contrastive Learning of Graph-Level Representations—0
Towards Federated Graph Learning in One-shot Communication—0
Freezing of Gait Detection Using Gramian Angular Fields and Federated Learning from Wearable SensorsCode0
A Potential Game Perspective in Federated LearningCode0
Efficient Federated Unlearning with Adaptive Differential Privacy Preservation—0
FedUHB: Accelerating Federated Unlearning via Polyak Heavy Ball Method—0
F^3OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics—0
Federated Learning for UAV-Based Spectrum Sensing: Enhancing Accuracy Through SNR-Weighted Model Aggregation—0
How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution—0
Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approach—0
Framework for Co-distillation Driven Federated Learning to Address Class Imbalance in HealthcareCode0
Embedding Byzantine Fault Tolerance into Federated Learning via Virtual Data-Driven Consistency Scoring PluginCode0
Evidential Federated Learning for Skin Lesion Image Classification—0
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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