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 2551–2575 of 6771 papers

Title	Date	Tasks	Status
Sparse-ProxSkip: Accelerated Sparse-to-Sparse Training in Federated Learning	May 31, 2024	Federated Learning	—Unverified
GI-NAS: Boosting Gradient Inversion Attacks through Adaptive Neural Architecture Search	May 31, 2024	Federated LearningNeural Architecture Search	—Unverified
ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning	May 31, 2024	Federated LearningModel Poisoning	—Unverified
Non-Federated Multi-Task Split Learning for Heterogeneous Sources	May 31, 2024	Federated Learningimage-classification	—Unverified
GANcrop: A Contrastive Defense Against Backdoor Attacks in Federated Learning	May 31, 2024	Contrastive LearningFederated Learning	—Unverified
Share Secrets for Privacy: Confidential Forecasting with Vertical Federated Learning	May 31, 2024	Federated LearningNavigate	CodeCode Available
Selective Knowledge Sharing for Personalized Federated Learning Under Capacity Heterogeneity	May 31, 2024	Federated LearningPersonalized Federated Learning	—Unverified
Enhancing Performance for Highly Imbalanced Medical Data via Data Regularization in a Federated Learning Setting	May 30, 2024	Disease PredictionFederated Learning	—Unverified
SPAM: Stochastic Proximal Point Method with Momentum Variance Reduction for Non-convex Cross-Device Federated Learning	May 30, 2024	Federated Learning	—Unverified
subMFL: Compatiple subModel Generation for Federated Learning in Device Heterogenous Environment	May 30, 2024	Federated LearningModel Compression	CodeCode Available
Gradient Inversion of Federated Diffusion Models	May 30, 2024	Federated LearningPrivacy Preserving	—Unverified
On Vessel Location Forecasting and the Effect of Federated Learning	May 30, 2024	Federated Learning	CodeCode Available
Cross-Training with Multi-View Knowledge Fusion for Heterogenous Federated Learning	May 30, 2024	Federated LearningRepresentation Learning	—Unverified
Federated Learning with Multi-resolution Model Broadcast	May 30, 2024	Federated Learningmodel	—Unverified
Federated and Transfer Learning for Cancer Detection Based on Image Analysis	May 30, 2024	Federated LearningTransfer Learning	—Unverified
Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspective	May 30, 2024	Federated LearningPrivacy Preserving	—Unverified
Enhancing Security and Privacy in Federated Learning using Low-Dimensional Update Representation and Proximity-Based Defense	May 29, 2024	Federated LearningPrivacy Preserving	—Unverified
Block-FeDL: Electric Vehicle Charging Load Forecasting using Federated Learning and Blockchain	May 29, 2024	Federated LearningLoad Forecasting	—Unverified
Optimizing Split Points for Error-Resilient SplitFed Learning	May 29, 2024	Federated LearningImage Segmentation	—Unverified
Differentially Private Clustered Federated Learning	May 29, 2024	ClusteringFairness	—Unverified
Federated Learning under Partially Class-Disjoint Data via Manifold Reshaping	May 29, 2024	Federated Learning	CodeCode Available
FedMAP: Unlocking Potential in Personalized Federated Learning through Bi-Level MAP Optimization	May 29, 2024	Federated LearningPersonalized Federated Learning	CodeCode Available
LoByITFL: Low Communication Secure and Private Federated Learning	May 29, 2024	Federated Learning	—Unverified
An Innovative Networks in Federated Learning	May 28, 2024	Computational EfficiencyFederated Learning	—Unverified
PeerFL: A Simulator for Peer-to-Peer Federated Learning at Scale	May 28, 2024	Federated Learning	CodeCode Available

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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