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 1826–1850 of 6771 papers

Title	Date	Tasks	Status	Hype
Federated Model Heterogeneous Matryoshka Representation Learning	Jun 1, 2024	Federated Learningmodel	—Unverified	0
Redefining Contributions: Shapley-Driven Federated Learning	Jun 1, 2024	Collaborative FairnessContribution Assessment	CodeCode Available	1
Non-Federated Multi-Task Split Learning for Heterogeneous Sources	May 31, 2024	Federated Learningimage-classification	—Unverified	0
Sheaf HyperNetworks for Personalized Federated Learning	May 31, 2024	Federated LearningMolecular Property Prediction	—Unverified	0
ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning	May 31, 2024	Federated LearningModel Poisoning	—Unverified	0
Sparse-ProxSkip: Accelerated Sparse-to-Sparse Training in Federated Learning	May 31, 2024	Federated Learning	—Unverified	0
Share Secrets for Privacy: Confidential Forecasting with Vertical Federated Learning	May 31, 2024	Federated LearningNavigate	CodeCode Available	0
GI-NAS: Boosting Gradient Inversion Attacks through Adaptive Neural Architecture Search	May 31, 2024	Federated LearningNeural Architecture Search	—Unverified	0
GANcrop: A Contrastive Defense Against Backdoor Attacks in Federated Learning	May 31, 2024	Contrastive LearningFederated Learning	—Unverified	0
Selective Knowledge Sharing for Personalized Federated Learning Under Capacity Heterogeneity	May 31, 2024	Federated LearningPersonalized Federated Learning	—Unverified	0
Pursuing Overall Welfare in Federated Learning through Sequential Decision Making	May 31, 2024	Decision MakingFairness	CodeCode Available	1
SPAM: Stochastic Proximal Point Method with Momentum Variance Reduction for Non-convex Cross-Device Federated Learning	May 30, 2024	Federated Learning	—Unverified	0
Gradient Inversion of Federated Diffusion Models	May 30, 2024	Federated LearningPrivacy Preserving	—Unverified	0
On Vessel Location Forecasting and the Effect of Federated Learning	May 30, 2024	Federated Learning	CodeCode Available	0
subMFL: Compatiple subModel Generation for Federated Learning in Device Heterogenous Environment	May 30, 2024	Federated LearningModel Compression	CodeCode Available	0
Federated Learning with Multi-resolution Model Broadcast	May 30, 2024	Federated Learningmodel	—Unverified	0
Enhancing Performance for Highly Imbalanced Medical Data via Data Regularization in a Federated Learning Setting	May 30, 2024	Disease PredictionFederated Learning	—Unverified	0
Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspective	May 30, 2024	Federated LearningPrivacy Preserving	—Unverified	0
Federated and Transfer Learning for Cancer Detection Based on Image Analysis	May 30, 2024	Federated LearningTransfer Learning	—Unverified	0
Cross-Training with Multi-View Knowledge Fusion for Heterogenous Federated Learning	May 30, 2024	Federated LearningRepresentation Learning	—Unverified	0
Block-FeDL: Electric Vehicle Charging Load Forecasting using Federated Learning and Blockchain	May 29, 2024	Federated LearningLoad Forecasting	—Unverified	0
LoByITFL: Low Communication Secure and Private Federated Learning	May 29, 2024	Federated Learning	—Unverified	0
Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization	May 29, 2024	Federated Learning	CodeCode Available	1
FedMAP: Unlocking Potential in Personalized Federated Learning through Bi-Level MAP Optimization	May 29, 2024	Federated LearningPersonalized Federated Learning	CodeCode Available	0
Optimizing Split Points for Error-Resilient SplitFed Learning	May 29, 2024	Federated LearningImage Segmentation	—Unverified	0

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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