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 13011350 of 6771 papers

TitleStatusHype
KnowledgeSG: Privacy-Preserving Synthetic Text Generation with Knowledge Distillation from ServerCode0
FairFML: Fair Federated Machine Learning with a Case Study on Reducing Gender Disparities in Cardiac Arrest Outcome PredictionCode0
Federated brain tumor segmentation: an extensive benchmark0
Over-the-Air Federated Learning in Cell-Free MIMO with Long-term Power Constraint0
Aiding Global Convergence in Federated Learning via Local Perturbation and Mutual Similarity Information0
Federated Learning Nodes Can Reconstruct Peers' Image Data0
FRIDA: Free-Rider Detection using Privacy Attacks0
FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models0
pFedGame -- Decentralized Federated Learning using Game Theory in Dynamic Topology0
ConDa: Fast Federated Unlearning with Contribution Dampening0
Deep Domain Isolation and Sample Clustered Federated Learning for Semantic SegmentationCode0
An Intelligent Native Network Slicing Security Architecture Empowered by Federated Learning0
BN-SCAFFOLD: controlling the drift of Batch Normalization statistics in Federated Learning0
FedMAC: Tackling Partial-Modality Missing in Federated Learning with Cross-Modal Aggregation and Contrastive Regularization0
A Survey on Group Fairness in Federated Learning: Challenges, Taxonomy of Solutions and Directions for Future ResearchCode0
Collaborative and Efficient Personalization with Mixtures of Adaptors0
Influence-oriented Personalized Federated Learning0
FedCert: Federated Accuracy CertificationCode0
FedStein: Enhancing Multi-Domain Federated Learning Through James-Stein EstimatorCode0
Accelerating Deep Learning with Fixed Time Budget0
FedPeWS: Personalized Warmup via Subnetworks for Enhanced Heterogeneous Federated Learning0
FedScalar: A Communication efficient Federated Learning0
Personalized Federated Learning for Generative AI-Assisted Semantic Communications0
Data Similarity-Based One-Shot Clustering for Multi-Task Hierarchical Federated Learning0
A Survey on Point-of-Interest Recommendation: Models, Architectures, and Security0
Towards Layer-Wise Personalized Federated Learning: Adaptive Layer Disentanglement via Conflicting Gradients0
Personalized Quantum Federated Learning for Privacy Image Classification0
Clinnova Federated Learning Proof of Concept: Key Takeaways from a Cross-border Collaboration0
A Federated Learning Platform as a Service for Advancing Stroke Management in European Clinical Centers0
Frequency-Based Federated Domain Generalization for Polyp SegmentationCode0
NTK-DFL: Enhancing Decentralized Federated Learning in Heterogeneous Settings via Neural Tangent Kernel0
Addressing Data Heterogeneity in Federated Learning with Adaptive Normalization-Free Feature Recalibration0
EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated LearningCode0
A Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoT0
On the Convergence of FedProx with Extrapolation and Inexact Prox0
FLAME: Adaptive and Reactive Concept Drift Mitigation for Federated Learning Deployments0
Overpredictive Signal Analytics in Federated Learning: Algorithms and Analysis0
Selective Aggregation for Low-Rank Adaptation in Federated LearningCode2
Debiasing Federated Learning with Correlated Client Participation0
FedPT: Federated Proxy-Tuning of Large Language Models on Resource-Constrained Edge Devices0
Quantized and Asynchronous Federated Learning0
Fisher Information-based Efficient Curriculum Federated Learning with Large Language Models0
Leveraging Pre-trained Models for Robust Federated Learning for Kidney Stone Type Recognition0
Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients0
HYDRA-FL: Hybrid Knowledge Distillation for Robust and Accurate Federated Learning0
Comments on "Privacy-Enhanced Federated Learning Against Poisoning Adversaries"0
Online Client Scheduling and Resource Allocation for Efficient Federated Edge Learning0
One Node Per User: Node-Level Federated Learning for Graph Neural Networks0
Tailored Federated Learning: Leveraging Direction Regulation & Knowledge Distillation0
Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and MethodCode1
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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