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 601650 of 6771 papers

TitleStatusHype
Federated nnU-Net for Privacy-Preserving Medical Image SegmentationCode1
Federated Learning Meets Fluid Antenna: Towards Robust and Scalable Edge Intelligence0
AugFL: Augmenting Federated Learning with Pretrained ModelsCode0
Leveraging Randomness in Model and Data Partitioning for Privacy Amplification0
Federated Learning for Privacy-Preserving Feedforward Control in Multi-Agent SystemsCode0
Federated Learning Framework via Distributed Mutual Learning0
GRAIN: Exact Graph Reconstruction from GradientsCode0
MAB-Based Channel Scheduling for Asynchronous Federated Learning in Non-Stationary Environments0
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 Perspective0
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 Strategies0
Communication-Efficient Device Scheduling for Federated Learning Using Lyapunov Optimization0
Asynchronous Personalized Federated Learning through Global Memorization0
Conditioning on Local Statistics for Scalable Heterogeneous Federated Learning0
FLStore: Efficient Federated Learning Storage for non-training workloadsCode0
FedDyMem: Efficient Federated Learning with Dynamic Memory and Memory-Reduce for Unsupervised Image Anomaly Detection0
Fed-KAN: Federated Learning with Kolmogorov-Arnold Networks for Traffic Prediction0
FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated ClientsCode1
QFAL: Quantum Federated Adversarial Learning0
FedMentalCare: Towards Privacy-Preserving Fine-Tuned LLMs to Analyze Mental Health Status Using Federated Learning Framework0
DPZV: Elevating the Tradeoff between Privacy and Utility in Zeroth-Order Vertical Federated Learning0
Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data0
Revisit the Stability of Vanilla Federated Learning Under Diverse Conditions0
Can Textual Gradient Work in Federated Learning?Code1
FAA-CLIP: Federated Adversarial Adaptation of CLIPCode0
Robust Over-the-Air Computation with Type-Based Multiple Access0
CLLoRA: An Approach to Measure the Effects of the Context Length for LLM Fine-Tuning0
H-FLTN: A Privacy-Preserving Hierarchical Framework for Electric Vehicle Spatio-Temporal Charge Prediction0
Personalized Federated Learning for Egocentric Video Gaze Estimation with Comprehensive Parameter Frezzing0
Differentially Private Federated Learning With Time-Adaptive Privacy Spending0
The Built-In Robustness of Decentralized Federated Averaging to Bad Data0
Design and implementation of a distributed security threat detection system integrating federated learning and multimodal LLM0
FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk0
Vision Language Models in Medicine0
FedBM: Stealing Knowledge from Pre-trained Language Models for Heterogeneous Federated LearningCode0
FedSV: Byzantine-Robust Federated Learning via Shapley Value0
Robust Federated Learning in Unreliable Wireless Networks: A Client Selection Approach0
Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning0
Robust Federated Learning with Global Sensitivity Estimation for Financial Risk Management0
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 Learning0
Multi-Target Federated Backdoor Attack Based on Feature Aggregation0
FedDA-TSformer: Federated Domain Adaptation with Vision TimeSformer for Left Ventricle Segmentation on Gated Myocardial Perfusion SPECT Image0
TrustChain: A Blockchain Framework for Auditing and Verifying Aggregators in Decentralized Federated Learning0
FedNIA: Noise-Induced Activation Analysis for Mitigating Data Poisoning in FL0
Toward Responsible Federated Large Language Models: Leveraging a Safety Filter and Constitutional AI0
SEAFL: Enhancing Efficiency in Semi-Asynchronous Federated Learning through Adaptive Aggregation and Selective Training0
FedORGP: Guiding Heterogeneous Federated Learning with Orthogonality Regularization on Global Prototypes0
A Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments0
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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