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 54515500 of 6771 papers

TitleStatusHype
FedHM: Efficient Federated Learning for Heterogeneous Models via Low-rank Factorization0
The Impact of Data Distribution on Fairness and Robustness in Federated LearningCode0
Robust Federated Learning for execution time-based device model identification under label-flipping attack0
Anomaly Localization in Model Gradients Under Backdoor Attacks Against Federated LearningCode0
Contextual Combinatorial Multi-output GP Bandits with Group Constraints0
Efficient Federated Learning for AIoT Applications Using Knowledge Distillation0
Fed2: Feature-Aligned Federated Learning0
Resource-Aware Asynchronous Online Federated Learning for Nonlinear Regression0
Dynamic Network-Assisted D2D-Aided Coded Distributed Learning0
Non-IID data and Continual Learning processes in Federated Learning: A long road ahead0
An Optimization Framework for Federated Edge Learning0
ExPLoit: Extracting Private Labels in Split Learning0
FedDropoutAvg: Generalizable federated learning for histopathology image classification0
On-Board Federated Learning for Dense LEO Constellations0
Forget-SVGD: Particle-Based Bayesian Federated Unlearning0
Jointly Learning from Decentralized (Federated) and Centralized Data to Mitigate Distribution Shift0
FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning0
Privacy-preserving Federated Adversarial Domain Adaption over Feature Groups for Interpretability0
Decentralized Unsupervised Learning of Visual Representations0
Federated Social Recommendation with Graph Neural Network0
Satellite Based Computing Networks with Federated Learning0
Federated Learning with Domain Generalization0
Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective0
Understanding Training-Data Leakage from Gradients in Neural Networks for Image ClassificationCode0
An Expectation-Maximization Perspective on Federated Learning0
Client Selection in Federated Learning based on Gradients Importance0
Over-the-Air Federated Learning with Retransmissions (Extended Version)0
A Novel Optimized Asynchronous Federated Learning FrameworkCode0
FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps0
A Vertical Federated Learning Method For Multi-Institutional Credit Scoring: MICS0
Privacy-preserving Federated Learning for Residential Short Term Load Forecasting0
FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated LearningCode0
Wyner-Ziv Gradient Compression for Federated Learning0
A Parameter Aggregation Strategy on Personalized Federated Learning0
Learning Tokenization in Private Federated Learning with Sub-Word Model Sampling0
FedParsing: a Semi-Supervised Federated Learning Model on Semantic Parsing0
On-Demand Unlabeled Personalized Federated Learning0
HADFL: Heterogeneity-aware Decentralized Federated Learning Framework0
Federated Learning for Smart Healthcare: A Survey0
FedCostWAvg: A new averaging for better Federated Learning0
Federated Learning for Internet of Things: Applications, Challenges, and Opportunities0
On the Tradeoff between Energy, Precision, and Accuracy in Federated Quantized Neural Networks0
DNN gradient lossless compression: Can GenNorm be the answer?Code0
Attentive Federated Learning for Concept Drift in Distributed 5G Edge NetworksCode0
Towards Privacy-Preserving Affect Recognition: A Two-Level Deep Learning Architecture0
Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges0
Federated Learning with Hyperparameter-based Clustering for Electrical Load Forecasting0
An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning0
STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural NetworksCode0
Hierarchical Bayesian Bandits0
Show:102550
← PrevPage 110 of 136Next →

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