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 43014350 of 6771 papers

TitleStatusHype
Towards Fairer and More Efficient Federated Learning via Multidimensional Personalized Edge Models0
Towards Model-Agnostic Federated Learning over NetworksCode0
Exploratory Analysis of Federated Learning Methods with Differential Privacy on MIMIC-III0
Improving the Model Consistency of Decentralized Federated Learning0
Federated Variational Inference Methods for Structured Latent Variable Models0
Federated Learning with Regularized Client Participation0
z-SignFedAvg: A Unified Stochastic Sign-based Compression for Federated Learning0
Adaptive Parameterization of Deep Learning Models for Federated Learning0
Cross-Fusion Rule for Personalized Federated Learning0
Federated Survival ForestsCode0
One-shot Empirical Privacy Estimation for Federated Learning0
On the Convergence of Federated Averaging with Cyclic Client Participation0
Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey0
Federated Privacy-preserving Collaborative Filtering for On-Device Next App Prediction0
Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning0
GAN-based Vertical Federated Learning for Label Protection in Binary Classification0
Use of Federated Learning and Blockchain towards Securing Financial Services0
Digital Over-the-Air Federated Learning in Multi-Antenna Systems0
FedSpectral+: Spectral Clustering using Federated Learning0
GTV: Generating Tabular Data via Vertical Federated Learning0
Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks0
Vertical Federated Learning: Taxonomies, Threats, and Prospects0
Convergence Analysis of Sequential Split Learning on Heterogeneous Data0
Instance-wise Batch Label Restoration via Gradients in Federated LearningCode0
FedLAP-DP: Federated Learning by Sharing Differentially Private Loss ApproximationsCode0
Federated Analytics: A survey0
On the Efficacy of Differentially Private Few-shot Image ClassificationCode0
Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy0
DoCoFL: Downlink Compression for Cross-Device Federated Learning0
Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-supervised Learning Approach0
FLSTRA: Federated Learning in Stratosphere0
CATFL: Certificateless Authentication-based Trustworthy Federated Learning for 6G Semantic Communications0
Truthful Incentive Mechanism for Federated Learning with Crowdsourced Data Labeling0
Distributed sequential federated learning0
FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation0
The Fair Value of Data Under Heterogeneous Privacy Constraints in Federated Learning0
Reliable Federated Disentangling Network for Non-IID Domain FeatureCode0
Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma SegmentationCode0
Federated Learning for Water Consumption Forecasting in Smart Cities0
Entropy-driven Fair and Effective Federated Learning0
FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust ClusteringCode0
Heterogeneous Datasets for Federated Survival Analysis SimulationCode0
CyclicFL: A Cyclic Model Pre-Training Approach to Efficient Federated Learning0
Uplink Scheduling in Federated Learning: an Importance-Aware Approach via Graph Representation Learning0
FedPH: Privacy-enhanced Heterogeneous Federated LearningCode0
SplitGNN: Splitting GNN for Node Classification with Heterogeneous Attention0
Time-sensitive Learning for Heterogeneous Federated Edge Intelligence0
Personalised Federated Learning On Heterogeneous Feature Spaces0
Interaction-level Membership Inference Attack Against Federated Recommender Systems0
Federated Learning over Coupled Graphs0
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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