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 20012050 of 6771 papers

TitleStatusHype
Gradient-Congruity Guided Federated Sparse Training0
Navigating Heterogeneity and Privacy in One-Shot Federated Learning with Diffusion Models0
Poisoning Attacks on Federated Learning for Autonomous Driving0
Recovering Labels from Local Updates in Federated Learning0
Sharp Bounds for Sequential Federated Learning on Heterogeneous DataCode0
Robust Decentralized Learning with Local Updates and Gradient Tracking0
WHALE-FL: Wireless and Heterogeneity Aware Latency Efficient Federated Learning over Mobile Devices via Adaptive Subnetwork Scheduling0
Quantum Federated Learning Experiments in the Cloud with Data Encoding0
Enhancing Mutual Trustworthiness in Federated Learning for Data-Rich Smart Cities0
Employing Federated Learning for Training Autonomous HVAC Systems0
Swarm Learning: A Survey of Concepts, Applications, and Trends0
Trust Driven On-Demand Scheme for Client Deployment in Federated Learning0
Detection of ransomware attacks using federated learning based on the CNN model0
PackVFL: Efficient HE Packing for Vertical Federated Learning0
FMLFS: A Federated Multi-Label Feature Selection Based on Information Theory in IoT Environment0
Federated Graph Learning for EV Charging Demand Forecasting with Personalization Against Cyberattacks0
URVFL: Undetectable Data Reconstruction Attack on Vertical Federated LearningCode0
Let's Focus: Focused Backdoor Attack against Federated Transfer Learning0
Fairness Without Demographics in Human-Centered Federated Learning0
An Aggregation-Free Federated Learning for Tackling Data Heterogeneity0
On the Impact of Data Heterogeneity in Federated Learning Environments with Application to Healthcare Networks0
Bridging Data Barriers among Participants: Assessing the Potential of Geoenergy through Federated Learning0
A Universal Metric of Dataset Similarity for Cross-silo Federated LearningCode0
FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition0
Joint Energy and Latency Optimization in Federated Learning over Cell-Free Massive MIMO Networks0
Energy-Efficient Federated Learning in Cooperative Communication within Factory Subnetworks0
FedCRL: Personalized Federated Learning with Contrastive Shared Representations for Label Heterogeneity in Non-IID Data0
pFedAFM: Adaptive Feature Mixture for Batch-Level Personalization in Heterogeneous Federated LearningCode0
TabVFL: Improving Latent Representation in Vertical Federated Learning0
Personalized Federated Learning via Sequential Layer Expansion in Representation Learning0
From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness MatchingCode1
Privacy-Preserving Aggregation for Decentralized Learning with Byzantine-Robustness0
Federated Learning and Differential Privacy Techniques on Multi-hospital Population-scale Electrocardiogram DataCode0
Beyond Traditional Threats: A Persistent Backdoor Attack on Federated LearningCode1
Federated Learning for Blind Image Super-Resolution0
On the Federated Learning Framework for Cooperative Perception0
Differentially Private Federated Learning: Servers Trustworthiness, Estimation, and Statistical Inference0
FedStyle: Style-Based Federated Learning Crowdsourcing Framework for Art CommissionsCode0
FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantification0
Brain Storm Optimization Based Swarm Learning for Diabetic Retinopathy Image Classification0
An Element-Wise Weights Aggregation Method for Federated LearningCode0
Blind Federated Learning without initial model0
Federated Learning with Only Positive Labels by Exploring Label Correlations0
Decentralized Personalized Federated Learning based on a Conditional Sparse-to-Sparser SchemeCode0
Collaborative Heterogeneous Causal Inference Beyond Meta-analysis0
Advances and Open Challenges in Federated Foundation Models0
FL-TAC: Enhanced Fine-Tuning in Federated Learning via Low-Rank, Task-Specific Adapter Clustering0
FedGreen: Carbon-aware Federated Learning with Model Size Adaptation0
Employing Layerwised Unsupervised Learning to Lessen Data and Loss Requirements in Forward-Forward Algorithms0
Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data0
Show:102550
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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