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 6201–6225 of 6771 papers

Title	Date	Tasks	Status	Hype
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients	Oct 3, 2020	Federated Learning	CodeCode Available	1
Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies	Oct 3, 2020	Distributed OptimizationFederated Learning	—Unverified	0
Towards Bidirectional Protection in Federated Learning	Oct 2, 2020	Federated Learning	—Unverified	0
Practical One-Shot Federated Learning for Cross-Silo Setting	Oct 2, 2020	Federated LearningTransfer Learning	CodeCode Available	1
Model-sharing Games: Analyzing Federated Learning Under Voluntary Participation	Oct 2, 2020	Federated Learning	CodeCode Available	1
Blind Federated Learning at the Wireless Edge with Low-Resolution ADC and DAC	Oct 1, 2020	Federated Learning	—Unverified	0
Optimal Task Assignment to Heterogeneous Federated Learning Devices	Oct 1, 2020	Federated Learning	CodeCode Available	0
Intrusion Detection with Segmented Federated Learning for Large-Scale Multiple LANs	Sep 28, 2020	Federated LearningIntrusion Detection	CodeCode Available	1
A Real-time Contribution Measurement Method for Participants in Federated Learning	Sep 28, 2020	Federated Learning	—Unverified	0
Model-Agnostic Round-Optimal Federated Learning via Knowledge Transfer	Sep 28, 2020	Federated LearningTransfer Learning	—Unverified	0
Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning	Sep 28, 2020	Automated Pancreas SegmentationDeep Learning	—Unverified	0
Loosely Coupled Federated Learning Over Generative Models	Sep 28, 2020	BIG-bench Machine LearningFederated Learning	—Unverified	0
Over-the-Air Federated Learning from Heterogeneous Data	Sep 27, 2020	Federated Learning	CodeCode Available	1
Federated Transfer Learning: concept and applications	Sep 26, 2020	Federated LearningTransfer Learning	—Unverified	0
Privacy-preserving Transfer Learning via Secure Maximum Mean Discrepancy	Sep 24, 2020	Federated LearningPrivacy Preserving	—Unverified	0
FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning	Sep 23, 2020	Federated LearningPrivacy Preserving	—Unverified	0
Dynamic Fusion based Federated Learning for COVID-19 Detection	Sep 22, 2020	BIG-bench Machine LearningDecision Making	—Unverified	0
FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling	Sep 22, 2020	Federated Learning	—Unverified	0
When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Network	Sep 22, 2020	Decision MakingDeep Reinforcement Learning	—Unverified	0
An Incentive Mechanism for Federated Learning in Wireless Cellular network: An Auction Approach	Sep 22, 2020	BIG-bench Machine LearningFederated Learning	—Unverified	0
Federated Learning for Computational Pathology on Gigapixel Whole Slide Images	Sep 21, 2020	Deep LearningDiagnostic	CodeCode Available	1
Training Production Language Models without Memorizing User Data	Sep 21, 2020	Federated LearningMemorization	—Unverified	0
Estimation of Individual Device Contributions for Incentivizing Federated Learning	Sep 20, 2020	Federated Learning	—Unverified	0
Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning	Sep 18, 2020	Adversarial RobustnessFederated Learning	—Unverified	0
Federated Learning with Nesterov Accelerated Gradient	Sep 18, 2020	Federated Learning	—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