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 63516400 of 6771 papers

TitleStatusHype
DAMe: Personalized Federated Social Event Detection with Dual Aggregation MechanismCode0
GAS: Generative Activation-Aided Asynchronous Split Federated LearningCode0
Mosaic: Data-Free Knowledge Distillation via Mixture-of-Experts for Heterogeneous Distributed EnvironmentsCode0
End-to-End Verifiable Decentralized Federated LearningCode0
Communication-Efficient Federated Learning via Clipped Uniform QuantizationCode0
Geminio: Language-Guided Gradient Inversion Attacks in Federated LearningCode0
Motley: Benchmarking Heterogeneity and Personalization in Federated LearningCode0
Federated Learning for Privacy-Preserving Feedforward Control in Multi-Agent SystemsCode0
Generalizable Learning Reconstruction for Accelerating MR Imaging via Federated Neural Architecture SearchCode0
BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoTCode0
Overcome Modal Bias in Multi-modal Federated Learning via Balanced Modality SelectionCode0
Redefining non-IID Data in Federated Learning for Computer Vision Tasks: Migrating from Labels to Embeddings for Task-Specific Data DistributionsCode0
Generalized Federated Learning via Sharpness Aware MinimizationCode0
Encrypted machine learning of molecular quantum propertiesCode0
Generalizing in Net-Zero Microgrids: A Study with Federated PPO and TRPOCode0
CXR-FL: Deep Learning-Based Chest X-ray Image Analysis Using Federated LearningCode0
MRI-based and metabolomics-based age scores act synergetically for mortality prediction shown by multi-cohort federated learningCode0
Generative AI-Powered Plugin for Robust Federated Learning in Heterogeneous IoT NetworksCode0
Federated Learning for Non-factorizable Models using Deep Generative Prior ApproximationsCode0
MrTF: Model Refinery for Transductive Federated LearningCode0
Generative Gradient Inversion via Over-Parameterized Networks in Federated LearningCode0
Federated Learning for Mobile Keyboard PredictionCode0
Genetic Algorithm-Based Dynamic Backdoor Attack on Federated Learning-Based Network Traffic ClassificationCode0
Genetic CFL: Optimization of Hyper-Parameters in Clustered Federated LearningCode0
Adaptive Client Selection with Personalization for Communication Efficient Federated LearningCode0
Federated Learning for Misbehaviour Detection with Variational Autoencoders and Gaussian Mixture ModelsCode0
Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry DetectionCode0
Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless NetworksCode0
Bayesian Robust Aggregation for Federated LearningCode0
PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated LearningCode0
TimberStrike: Dataset Reconstruction Attack Revealing Privacy Leakage in Federated Tree-Based SystemsCode0
Enable the Right to be Forgotten with Federated Client Unlearning in Medical ImagingCode0
Federated Learning for Keyword SpottingCode0
Subspace Constraint and Contribution Estimation for Heterogeneous Federated LearningCode0
Federated Learning for Data StreamsCode0
Empowering Data Mesh with Federated LearningCode0
Embedding Byzantine Fault Tolerance into Federated Learning via Virtual Data-Driven Consistency Scoring PluginCode0
GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph DataCode0
Federated learning compression designed for lightweight communicationsCode0
Global Balanced Experts for Federated Long-Tailed LearningCode0
PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its applications on real-world medical recordsCode0
Global Group Fairness in Federated Learning via Function TrackingCode0
Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning ApplicationsCode0
Cross-silo Federated Learning with Record-level Personalized Differential PrivacyCode0
Global Layers: Non-IID Tabular Federated LearningCode0
Universally Harmonizing Differential Privacy Mechanisms for Federated Learning: Boosting Accuracy and ConvergenceCode0
Cross-Silo Federated Learning Across Divergent Domains with Iterative Parameter AlignmentCode0
Self-organizing Democratized Learning: Towards Large-scale Distributed Learning SystemsCode0
GLow -- A Novel, Flower-Based Simulated Gossip Learning StrategyCode0
Reducing Training Time in Cross-Silo Federated Learning using Multigraph TopologyCode0
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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