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 601–650 of 6771 papers

TitleStatusHype
Federated nnU-Net for Privacy-Preserving Medical Image SegmentationCode1
Federated Learning Meets Fluid Antenna: Towards Robust and Scalable Edge Intelligence—0
AugFL: Augmenting Federated Learning with Pretrained ModelsCode0
Leveraging Randomness in Model and Data Partitioning for Privacy Amplification—0
Federated Learning for Privacy-Preserving Feedforward Control in Multi-Agent SystemsCode0
Federated Learning Framework via Distributed Mutual Learning—0
GRAIN: Exact Graph Reconstruction from GradientsCode0
MAB-Based Channel Scheduling for Asynchronous Federated Learning in Non-Stationary Environments—0
A Lightweight and Secure Deep Learning Model for Privacy-Preserving Federated Learning in Intelligent EnterprisesCode0
Heterogeneity Matters even More in Distributed Learning: Study from Generalization Perspective—0
Patient-Level Anatomy Meets Scanning-Level Physics: Personalized Federated Low-Dose CT Denoising Empowered by Large Language ModelCode0
Unmasking Digital Falsehoods: A Comparative Analysis of LLM-Based Misinformation Detection Strategies—0
Communication-Efficient Device Scheduling for Federated Learning Using Lyapunov Optimization—0
Asynchronous Personalized Federated Learning through Global Memorization—0
Conditioning on Local Statistics for Scalable Heterogeneous Federated Learning—0
FLStore: Efficient Federated Learning Storage for non-training workloadsCode0
FedDyMem: Efficient Federated Learning with Dynamic Memory and Memory-Reduce for Unsupervised Image Anomaly Detection—0
Fed-KAN: Federated Learning with Kolmogorov-Arnold Networks for Traffic Prediction—0
FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated ClientsCode1
QFAL: Quantum Federated Adversarial Learning—0
FedMentalCare: Towards Privacy-Preserving Fine-Tuned LLMs to Analyze Mental Health Status Using Federated Learning Framework—0
DPZV: Elevating the Tradeoff between Privacy and Utility in Zeroth-Order Vertical Federated Learning—0
Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data—0
Revisit the Stability of Vanilla Federated Learning Under Diverse Conditions—0
Can Textual Gradient Work in Federated Learning?Code1
FAA-CLIP: Federated Adversarial Adaptation of CLIPCode0
Robust Over-the-Air Computation with Type-Based Multiple Access—0
CLLoRA: An Approach to Measure the Effects of the Context Length for LLM Fine-Tuning—0
H-FLTN: A Privacy-Preserving Hierarchical Framework for Electric Vehicle Spatio-Temporal Charge Prediction—0
Personalized Federated Learning for Egocentric Video Gaze Estimation with Comprehensive Parameter Frezzing—0
Differentially Private Federated Learning With Time-Adaptive Privacy Spending—0
The Built-In Robustness of Decentralized Federated Averaging to Bad Data—0
Design and implementation of a distributed security threat detection system integrating federated learning and multimodal LLM—0
FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk—0
Vision Language Models in Medicine—0
FedBM: Stealing Knowledge from Pre-trained Language Models for Heterogeneous Federated LearningCode0
FedSV: Byzantine-Robust Federated Learning via Shapley Value—0
Robust Federated Learning in Unreliable Wireless Networks: A Client Selection Approach—0
Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning—0
Robust Federated Learning with Global Sensitivity Estimation for Financial Risk Management—0
Forgetting Any Data at Any Time: A Theoretically Certified Unlearning Framework for Vertical Federated LearningCode0
VGFL-SA: Vertical Graph Federated Learning Structure Attack Based on Contrastive Learning—0
Multi-Target Federated Backdoor Attack Based on Feature Aggregation—0
FedDA-TSformer: Federated Domain Adaptation with Vision TimeSformer for Left Ventricle Segmentation on Gated Myocardial Perfusion SPECT Image—0
TrustChain: A Blockchain Framework for Auditing and Verifying Aggregators in Decentralized Federated Learning—0
FedNIA: Noise-Induced Activation Analysis for Mitigating Data Poisoning in FL—0
Toward Responsible Federated Large Language Models: Leveraging a Safety Filter and Constitutional AI—0
SEAFL: Enhancing Efficiency in Semi-Asynchronous Federated Learning through Adaptive Aggregation and Selective Training—0
FedORGP: Guiding Heterogeneous Federated Learning with Orthogonality Regularization on Global Prototypes—0
A Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments—0
Show:102550
← PrevPage 13 of 136Next →

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