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 6626–6650 of 6771 papers

Title	Date	Tasks	Status	Hype
A blockchain-orchestrated Federated Learning architecture for healthcare consortia	Oct 12, 2019	Federated LearningPrivacy Preserving	—Unverified	0
Central Server Free Federated Learning over Single-sided Trust Social Networks	Oct 11, 2019	Federated Learning	CodeCode Available	0
High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning	Oct 9, 2019	Federated LearningQuantization	—Unverified	0
Federated Learning of N-gram Language Models	Oct 8, 2019	Federated LearningLanguage Modelling	—Unverified	0
Accelerating Federated Learning via Momentum Gradient Descent	Oct 8, 2019	BIG-bench Machine LearningFederated Learning	—Unverified	0
FedMD: Heterogenous Federated Learning via Model Distillation	Oct 8, 2019	Federated LearningKnowledge Distillation	CodeCode Available	1
Differential Privacy-enabled Federated Learning for Sensitive Health Data	Oct 7, 2019	Federated Learning	—Unverified	0
Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligence	Oct 4, 2019	BIG-bench Machine LearningFederated Learning	—Unverified	0
Privacy Preserving Stochastic Channel-Based Federated Learning with Neural Network Pruning	Oct 4, 2019	Federated LearningNetwork Pruning	—Unverified	0
Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints	Oct 4, 2019	ClusteringFederated Learning	CodeCode Available	0
SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead	Oct 3, 2019	Federated Learning	—Unverified	0
Privacy-preserving Federated Brain Tumour Segmentation	Oct 2, 2019	Federated LearningPrivacy Preserving	—Unverified	0
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization	Sep 28, 2019	Federated LearningQuantization	—Unverified	0
Federated User Representation Learning	Sep 27, 2019	Federated LearningPrivacy Preserving	—Unverified	0
Improving Federated Learning Personalization via Model Agnostic Meta Learning	Sep 27, 2019	Federated LearningMeta-Learning	CodeCode Available	0
Active Federated Learning	Sep 27, 2019	Federated Learning	—Unverified	0
Federated Learning in Mobile Edge Networks: A Comprehensive Survey	Sep 26, 2019	Edge-computingFederated Learning	—Unverified	0
Model Pruning Enables Efficient Federated Learning on Edge Devices	Sep 26, 2019	Federated Learning	CodeCode Available	0
On Federated Learning of Deep Networks from Non-IID Data: Parameter Divergence and the Effects of Hyperparametric Methods	Sep 25, 2019	Federated LearningHyperparameter Optimization	—Unverified	0
Low Rank Training of Deep Neural Networks for Emerging Memory Technology	Sep 25, 2019	Computational EfficiencyDecision Making	—Unverified	0
Optimal query complexity for private sequential learning against eavesdropping	Sep 21, 2019	Federated Learning	—Unverified	0
Towards Federated Graph Learning for Collaborative Financial Crimes Detection	Sep 19, 2019	Federated LearningGraph Learning	—Unverified	0
Detailed comparison of communication efficiency of split learning and federated learning	Sep 18, 2019	Federated Learning	—Unverified	0
Measure Contribution of Participants in Federated Learning	Sep 17, 2019	BIG-bench Machine LearningFeature Importance	—Unverified	0
A Joint Learning and Communications Framework for Federated Learning over Wireless Networks	Sep 17, 2019	Federated Learning	CodeCode Available	0

Show:10 25 50

← PrevPage 266 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