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 46514700 of 6771 papers

TitleStatusHype
GI-NAS: Boosting Gradient Inversion Attacks through Adaptive Neural Architecture Search0
GI-SMN: Gradient Inversion Attack against Federated Learning without Prior Knowledge0
GitFL: Adaptive Asynchronous Federated Learning using Version Control0
Global Convergence of Federated Learning for Mixed Regression0
Global Intervention and Distillation for Federated Out-of-Distribution Generalization0
Local-Global Knowledge Distillation in Heterogeneous Federated Learning with Non-IID Data0
Global Multiclass Classification and Dataset Construction via Heterogeneous Local Experts0
Global Update Guided Federated Learning0
GLOCALFAIR: Jointly Improving Global and Local Group Fairness in Federated Learning0
GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning0
Goal-oriented Communications for the IoT: System Design and Adaptive Resource Optimization0
Goal-Oriented Communications in Federated Learning via Feedback on Risk-Averse Participation0
Goal-Oriented Wireless Communication Resource Allocation for Cyber-Physical Systems0
GOF-TTE: Generative Online Federated Learning Framework for Travel Time Estimation0
GowFed -- A novel Federated Network Intrusion Detection System0
GPFL: A Gradient Projection-Based Client Selection Framework for Efficient Federated Learning0
GP-FL: Model-Based Hessian Estimation for Second-Order Over-the-Air Federated Learning0
Gradient and Channel Aware Dynamic Scheduling for Over-the-Air Computation in Federated Edge Learning Systems0
Gradient-Congruity Guided Federated Sparse Training0
Gradient Descent with Compressed Iterates0
Mjolnir: Breaking the Shield of Perturbation-Protected Gradients via Adaptive Diffusion0
Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning0
A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systems0
ExPLoit: Extracting Private Labels in Split Learning0
Gradient Inversion Attack on Graph Neural Networks0
Gradient Inversion of Federated Diffusion Models0
Gradient-less Federated Gradient Boosting Trees with Learnable Learning Rates0
Gradient Masked Averaging for Federated Learning0
Gradient Masked Federated Optimization0
Gradient Obfuscation Gives a False Sense of Security in Federated Learning0
Gradient Purification: Defense Against Poisoning Attack in Decentralized Federated Learning0
Gradient Sparification for Asynchronous Distributed Training0
Gradient Sparsification for Efficient Wireless Federated Learning with Differential Privacy0
Gradient Statistics Aware Power Control for Over-the-Air Federated Learning0
GradualDiff-Fed: A Federated Learning Specialized Framework for Large Language Model0
Gradual Federated Learning with Simulated Annealing0
GRAFFL: Gradient-free Federated Learning of a Bayesian Generative Model0
Graph-Assisted Communication-Efficient Ensemble Federated Learning0
Graph Federated Learning Based on the Decentralized Framework0
Graph Federated Learning Based Proactive Content Caching in Edge Computing0
Graph Federated Learning with Hidden Representation Sharing0
GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs0
GraphGANFed: A Federated Generative Framework for Graph-Structured Molecules Towards Efficient Drug Discovery0
Green Federated Learning0
Green Federated Learning: A new era of Green Aware AI0
Green Federated Learning Over Cloud-RAN with Limited Fronthual Capacity and Quantized Neural Networks0
Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Design0
Ground-Assisted Federated Learning in LEO Satellite Constellations0
Grounding Foundation Models through Federated Transfer Learning: A General Framework0
Group Personalized Federated Learning0
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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