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 10011050 of 6771 papers

TitleStatusHype
Privacy-Preserving in Medical Image Analysis: A Review of Methods and Applications0
Federated Automated Feature Engineering0
BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoTCode0
FedMetaMed: Federated Meta-Learning for Personalized Medication in Distributed Healthcare Systems0
FedDUAL: A Dual-Strategy with Adaptive Loss and Dynamic Aggregation for Mitigating Data Heterogeneity in Federated LearningCode0
Federated Learning in Mobile Networks: A Comprehensive Case Study on Traffic ForecastingCode2
Providing Differential Privacy for Federated Learning Over Wireless: A Cross-layer Framework0
GP-FL: Model-Based Hessian Estimation for Second-Order Over-the-Air Federated Learning0
Beyond Local Sharpness: Communication-Efficient Global Sharpness-aware Minimization for Federated LearningCode2
Reactive Orchestration for Hierarchical Federated Learning Under a Communication Cost BudgetCode0
Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization0
Proximal Control of UAVs with Federated Learning for Human-Robot Collaborative Domains0
Towards the efficacy of federated prediction for epidemics on networksCode0
Learn More by Using Less: Distributed Learning with Energy-Constrained Devices0
PAPAYA Federated Analytics Stack: Engineering Privacy, Scalability and Practicality0
Defending Against Diverse Attacks in Federated Learning Through Consensus-Based Bi-Level OptimizationCode0
Fractional Order Distributed Optimization0
Review of Mathematical Optimization in Federated Learning0
HumekaFL: Automated Detection of Neonatal Asphyxia Using Federated Learning0
Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer InterfacesCode0
FedAH: Aggregated Head for Personalized Federated LearningCode0
FedPAW: Federated Learning with Personalized Aggregation Weights for Urban Vehicle Speed PredictionCode0
Privacy-Preserving Federated Learning via Homomorphic Adversarial Networks0
Towards Privacy-Preserving Medical Imaging: Federated Learning with Differential Privacy and Secure Aggregation Using a Modified ResNet Architecture0
Incentivizing Truthful Collaboration in Heterogeneous Federated Learning0
A Comprehensive Guide to Explainable AI: From Classical Models to LLMsCode2
IRS Aided Federated Learning: Multiple Access and Fundamental Tradeoff0
MQFL-FHE: Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryption0
Rethinking the initialization of Momentum in Federated Learning with Heterogeneous Data0
Gradient Inversion Attack on Graph Neural Networks0
Streamlined Federated Unlearning: Unite as One to Be Highly Efficient0
Swarm Intelligence-Driven Client Selection for Federated Learning in Cybersecurity applications0
Controlling Participation in Federated Learning with FeedbackCode0
PEFT-as-an-Attack! Jailbreaking Language Models during Federated Parameter-Efficient Fine-Tuning0
Personalized Federated Fine-Tuning for LLMs via Data-Driven Heterogeneous Model ArchitecturesCode1
Locally Differentially Private Online Federated Learning With Correlated Noise0
Task Arithmetic Through The Lens Of One-Shot Federated Learning0
Federated Learning with Uncertainty and Personalization via Efficient Second-order Optimization0
Hidden Data Privacy Breaches in Federated Learning0
A High Energy-Efficiency Multi-core Neuromorphic Architecture for Deep SNN Training0
Distributed Sign Momentum with Local Steps for Training TransformersCode0
Adaptive Client Selection with Personalization for Communication Efficient Federated LearningCode0
Towards Efficient Model-Heterogeneity Federated Learning for Large Models0
Distributed Online Optimization with Stochastic Agent Availability0
BadSFL: Backdoor Attack against Scaffold Federated Learning0
Understanding Generalization of Federated Learning: the Trade-off between Model Stability and Optimization0
Distributed, communication-efficient, and differentially private estimation of KL divergence0
Privacy-Preserving Federated Foundation Model for Generalist Ultrasound Artificial Intelligence0
An Empirical Study of Vulnerability Detection using Federated Learning0
TIFeD: a Tiny Integer-based Federated learning algorithm with Direct feedback alignmentCode0
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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