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 651–700 of 6771 papers

TitleStatusHype
Orthogonal Calibration for Asynchronous Federated Learning—0
FedMobile: Enabling Knowledge Contribution-aware Multi-modal Federated Learning with Incomplete Modalities—0
Accurate Forgetting for Heterogeneous Federated Continual LearningCode0
Distributed U-net model and Image Segmentation for Lung Cancer Detection—0
VFL-RPS: Relevant Participant Selection in Vertical Federated Learning—0
Federated Fine-Tuning of Large Language Models: Kahneman-Tversky vs. Direct Preference Optimization—0
Vision Foundation Models in Medical Image Analysis: Advances and Challenges—0
Model Inversion Attack against Federated Unlearning—0
Blockchain-based Framework for Scalable and Incentivized Federated Learning—0
Homophily Heterogeneity Matters in Graph Federated Learning: A Spectrum Sharing and Complementing Perspective—0
Smoothed Normalization for Efficient Distributed Private Optimization—0
Federated Variational Inference for Bayesian Mixture Models—0
Fluid Antenna Enabled Over-the-Air Federated Learning: Joint Optimization of Positioning, Beamforming, and User Selection—0
A new framework for prognostics in decentralized industries: Enhancing fairness, security, and transparency through Blockchain and Federated Learning—0
FedEAT: A Robustness Optimization Framework for Federated LLMs—0
Double Momentum and Error Feedback for Clipping with Fast Rates and Differential Privacy—0
Ten Challenging Problems in Federated Foundation Models—0
Efficient Zero-Order Federated Finetuning of Language Models for Resource-Constrained Devices—0
ClusMFL: A Cluster-Enhanced Framework for Modality-Incomplete Multimodal Federated Learning in Brain Imaging Analysis—0
Federated Learning-Driven Cybersecurity Framework for IoT Networks with Privacy-Preserving and Real-Time Threat Detection Capabilities—0
Fine-Tuning Foundation Models with Federated Learning for Privacy Preserving Medical Time Series Forecasting—0
Vertical Federated Continual Learning via Evolving Prototype Knowledge—0
One-shot Federated Learning Methods: A Practical GuideCode0
Use of Air Quality Sensor Network Data for Real-time Pollution-Aware POI SuggestionCode0
Towards Seamless Hierarchical Federated Learning under Intermittent Client Participation: A Stagewise Decision-Making Methodology—0
RLSA-PFL: Robust Lightweight Secure Aggregation with Model Inconsistency Detection in Privacy-Preserving Federated Learning—0
Representation Learning to Advance Multi-institutional Studies with Electronic Health Record Data—0
FBFL: A Field-Based Coordination Approach for Data Heterogeneity in Federated LearningCode0
Vertical Federated Learning in Practice: The Good, the Bad, and the Ugly—0
One-Shot Federated Learning with Classifier-Free Diffusion Models—0
PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated LearningCode0
FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge DevicesCode0
Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy—0
Optimizing Asynchronous Federated Learning: A~Delicate Trade-Off Between Model-Parameter Staleness and Update Frequency—0
SLVR: Securely Leveraging Client Validation for Robust Federated Learning—0
An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language ModelsCode0
PFedDST: Personalized Federated Learning with Decentralized Selection Training—0
FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data—0
Unveiling Client Privacy Leakage from Public Dataset Usage in Federated Distillation—0
Initialization Matters: Unraveling the Impact of Pre-Training on Federated Learning—0
Federated Self-supervised Domain Generalization for Label-efficient Polyp Segmentation—0
Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT—0
Many-Task Federated Fine-Tuning via Unified Task Vectors—0
Federated Continual Learning: Concepts, Challenges, and Solutions—0
Krum Federated Chain (KFC): Using blockchain to defend against adversarial attacks in Federated LearningCode0
Analytic Personalized Federated Meta-Learning—0
Federated Sinkhorn—0
Meta-Computing Enhanced Federated Learning in IIoT: Satisfaction-Aware Incentive Scheme via DRL-Based Stackelberg Game—0
Fine-tuning Multimodal Transformers on Edge: A Parallel Split Learning Approach—0
DROP: Poison Dilution via Knowledge Distillation for Federated LearningCode0
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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