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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 3001–3025 of 6771 papers

Title	Date	Tasks	Status	Hype
Energy-Aware Federated Learning with Distributed User Sampling and Multichannel ALOHA	Sep 12, 2023	Federated Learning	—Unverified	0
Towards Federated Learning Under Resource Constraints via Layer-wise Training and Depth Dropout	Sep 11, 2023	Federated LearningRepresentation Learning	—Unverified	0
Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning	Sep 11, 2023	Federated Learningimage-classification	CodeCode Available	1
Advancing Federated Learning in 6G: A Trusted Architecture with Graph-based Analysis	Sep 11, 2023	Federated LearningGraph Neural Network	—Unverified	0
Federated Learning Incentive Mechanism under Buyers' Auction Market	Sep 10, 2023	Federated Learning	—Unverified	0
Privacy Preserving Federated Learning with Convolutional Variational Bottlenecks	Sep 8, 2023	Federated Learningimage-classification	—Unverified	0
Federated Learning for Early Dropout Prediction on Healthy Ageing Applications	Sep 8, 2023	Federated Learning	—Unverified	0
Enabling Intelligent Vehicular Networks Through Distributed Learning in the Non-Terrestrial Networks 6G Vision	Sep 7, 2023	Edge-computingFederated Learning	—Unverified	0
Byzantine-Robust Federated Learning with Variance Reduction and Differential Privacy	Sep 7, 2023	Federated Learning	—Unverified	0
On the dynamics of multi agent nonlinear filtering and learning	Sep 7, 2023	Federated Learning	—Unverified	0
Sparse Federated Training of Object Detection in the Internet of Vehicles	Sep 7, 2023	Federated LearningObject	—Unverified	0
Privacy-preserving Continual Federated Clustering via Adaptive Resonance Theory	Sep 7, 2023	ClusteringContinual Learning	CodeCode Available	0
Federated Learning Over Images: Vertical Decompositions and Pre-Trained Backbones Are Difficult to Beat	Sep 6, 2023	ClassificationFederated Learning	—Unverified	0
EdgeFL: A Lightweight Decentralized Federated Learning Framework	Sep 6, 2023	Federated Learning	—Unverified	0
Distributionally Robust Learning for Multi-source Unsupervised Domain Adaptation	Sep 5, 2023	Domain AdaptationFederated Learning	CodeCode Available	0
Bias Propagation in Federated Learning	Sep 5, 2023	FairnessFederated Learning	CodeCode Available	1
Personalized Federated Deep Reinforcement Learning-based Trajectory Optimization for Multi-UAV Assisted Edge Computing	Sep 5, 2023	Deep Reinforcement LearningEdge-computing	—Unverified	0
Secure and Efficient Federated Learning in LEO Constellations using Decentralized Key Generation and On-Orbit Model Aggregation	Sep 4, 2023	Federated LearningPrivacy Preserving	—Unverified	0
DRAG: Divergence-based Adaptive Aggregation in Federated learning on Non-IID Data	Sep 4, 2023	Federated Learning	—Unverified	0
Adaptive Model Pruning and Personalization for Federated Learning over Wireless Networks	Sep 4, 2023	Federated Learning	—Unverified	0
Federated cINN Clustering for Accurate Clustered Federated Learning	Sep 4, 2023	ClusteringFederated Learning	—Unverified	0
Composite federated learning with heterogeneous data	Sep 4, 2023	Federated Learning	—Unverified	0
Communication-Efficient Design of Learning System for Energy Demand Forecasting of Electrical Vehicles	Sep 4, 2023	Demand ForecastingFederated Learning	CodeCode Available	0
Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learning	Sep 3, 2023	Continual LearningFederated Learning	—Unverified	0
A Comparative Evaluation of FedAvg and Per-FedAvg Algorithms for Dirichlet Distributed Heterogeneous Data	Sep 3, 2023	Federated Learning	—Unverified	0

Show:10 25 50

← PrevPage 121 of 271Next →

All datasets Cityscapes heterogeneous Landmarks-User-160k CIFAR-100 (alpha=0, 10 clients per round)CIFAR-100 (alpha=0, 20 clients per round)CIFAR-100 (alpha=0.5, 10 clients per round)CIFAR-100 (alpha=0.5, 20 clients per round)CIFAR-100 (alpha=0.5, 5 clients per round)CIFAR-100 (alpha=0, 5 clients per round)CIFAR-100 (alpha=1000, 10 clients per round)CIFAR-100 (alpha=1000, 20 clients per round)CIFAR-100 (alpha=1000, 5 clients per round)CIFAR100 (alpha=0.3, 10 clients per round)

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	SiloBN + ASAM	mIoU	49.75	—	Unverified
2	SiloBN + SAM	mIoU	49.1	—	Unverified
3	SiloBN	mIoU	45.96	—	Unverified
4	FedSAM + SWA	mIoU	43.42	—	Unverified
5	FedASAM + SWA	mIoU	43.02	—	Unverified
6	FedAvg + SWA	mIoU	42.48	—	Unverified
7	FedASAM	mIoU	42.27	—	Unverified
8	FedSAM	mIoU	41.22	—	Unverified
9	FedAvg	mIoU	38.65	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	Acc@1-1262Clients	68.32	—	Unverified
2	FedSAM + SWA	Acc@1-1262Clients	68.12	—	Unverified
3	FedAvg + SWA	Acc@1-1262Clients	67.52	—	Unverified
4	FedASAM	Acc@1-1262Clients	64.23	—	Unverified
5	FedSAM	Acc@1-1262Clients	63.72	—	Unverified
6	FedAvg	Acc@1-1262Clients	61.91	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	42.64	—	Unverified
2	FedASAM	ACC@1-100Clients	39.76	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	39.51	—	Unverified
4	FedSAM	ACC@1-100Clients	36.93	—	Unverified
5	FedAvg	ACC@1-100Clients	36.74	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	41.62	—	Unverified
2	FedASAM	ACC@1-100Clients	40.81	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	39.24	—	Unverified
4	FedAvg	ACC@1-100Clients	38.59	—	Unverified
5	FedSAM	ACC@1-100Clients	38.56	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	48.72	—	Unverified
2	FedSAM + SWA	ACC@1-100Clients	46.76	—	Unverified
3	FedASAM	ACC@1-100Clients	46.58	—	Unverified
4	FedSAM	ACC@1-100Clients	44.84	—	Unverified
5	FedAvg	ACC@1-100Clients	41.27	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	48.27	—	Unverified
2	FedASAM	ACC@1-100Clients	47.78	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	46.47	—	Unverified
4	FedSAM	ACC@1-100Clients	46.05	—	Unverified
5	FedAvg	ACC@1-100Clients	42.17	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	49.17	—	Unverified
2	FedSAM + SWA	ACC@1-100Clients	47.96	—	Unverified
3	FedASAM	ACC@1-100Clients	45.61	—	Unverified
4	FedSAM	ACC@1-100Clients	44.73	—	Unverified
5	FedAvg	ACC@1-100Clients	40.43	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	42.01	—	Unverified
2	FedSAM + SWA	ACC@1-100Clients	39.3	—	Unverified
3	FedASAM	ACC@1-100Clients	36.04	—	Unverified
4	FedSAM	ACC@1-100Clients	31.04	—	Unverified
5	FedAvg	ACC@1-100Clients	30.25	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM	ACC@1-100Clients	54.97	—	Unverified
2	FedASAM + SWA	ACC@1-100Clients	54.79	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	53.67	—	Unverified
4	FedSAM	ACC@1-100Clients	53.39	—	Unverified
5	FedAvg	ACC@1-100Clients	50.25	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM	ACC@1-100Clients	54.5	—	Unverified
2	FedSAM + SWA	ACC@1-100Clients	54.36	—	Unverified
3	FedASAM + SWA	ACC@1-100Clients	54.1	—	Unverified
4	FedSAM	ACC@1-100Clients	53.97	—	Unverified
5	FedAvg	ACC@1-100Clients	50.66	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM	ACC@1-100Clients	54.81	—	Unverified
2	FedSAM	ACC@1-100Clients	54.01	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	53.9	—	Unverified
4	FedASAM + SWA	ACC@1-100Clients	53.86	—	Unverified
5	FedAvg	ACC@1-100Clients	49.92	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	AdaBest	Average Top-1 Accuracy	56.2	—	Unverified