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 6526–6550 of 6771 papers

Title	Date	Tasks	Status	Hype
Federated Matrix Factorization: Algorithm Design and Application to Data Clustering	Feb 12, 2020	ClusteringFederated Learning	—Unverified	0
Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning	Feb 11, 2020	Federated Learning	—Unverified	0
Federated Learning of a Mixture of Global and Local Models	Feb 10, 2020	Federated Learning	—Unverified	0
MDLdroid: a ChainSGD-reduce Approach to Mobile Deep Learning for Personal Mobile Sensing	Feb 7, 2020	Deep LearningFederated Learning	—Unverified	0
Faster On-Device Training Using New Federated Momentum Algorithm	Feb 6, 2020	Federated Learning	—Unverified	0
Knowledge Federation: A Unified and Hierarchical Privacy-Preserving AI Framework	Feb 5, 2020	Federated LearningPrivacy Preserving	—Unverified	0
Prophet: Proactive Candidate-Selection for Federated Learning by Predicting the Qualities of Training and Reporting Phases	Feb 3, 2020	Deep Reinforcement LearningFederated Learning	—Unverified	0
Learning to Detect Malicious Clients for Robust Federated Learning	Feb 1, 2020	Federated Learningimage-classification	—Unverified	0
Multi-Participant Multi-Class Vertical Federated Learning	Jan 30, 2020	feature selectionFederated Learning	—Unverified	0
FOCUS: Dealing with Label Quality Disparity in Federated Learning	Jan 29, 2020	Federated LearningPrivacy Preserving	—Unverified	0
Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge	Jan 28, 2020	Federated LearningScheduling	—Unverified	0
TiFL: A Tier-based Federated Learning System	Jan 25, 2020	Federated Learning	—Unverified	0
RPN: A Residual Pooling Network for Efficient Federated Learning	Jan 23, 2020	Federated Learning	—Unverified	0
Communication Efficient Federated Learning over Multiple Access Channels	Jan 23, 2020	Federated LearningInformativeness	—Unverified	0
A Federated Deep Learning Framework for Privacy Preservation and Communication Efficiency	Jan 22, 2020	Federated LearningPrivacy Preserving	—Unverified	0
Convergence Time Optimization for Federated Learning over Wireless Networks	Jan 22, 2020	Federated Learning	—Unverified	0
Stratified cross-validation for unbiased and privacy-preserving federated learning	Jan 22, 2020	Federated LearningPrivacy Preserving	—Unverified	0
Intermittent Pulling with Local Compensation for Communication-Efficient Federated Learning	Jan 22, 2020	BIG-bench Machine LearningFederated Learning	—Unverified	0
Overcoming Noisy and Irrelevant Data in Federated Learning	Jan 22, 2020	Federated Learning	—Unverified	0
Combining Federated and Active Learning for Communication-efficient Distributed Failure Prediction in Aeronautics	Jan 21, 2020	Active LearningFederated Learning	—Unverified	0
FedVision: An Online Visual Object Detection Platform Powered by Federated Learning	Jan 17, 2020	Federated Learningobject-detection	CodeCode Available	0
Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results	Jan 16, 2020	Domain AdaptationFederated Learning	CodeCode Available	1
Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach	Jan 14, 2020	FairnessFederated Learning	—Unverified	0
Exploiting Unlabeled Data in Smart Cities using Federated Learning	Jan 10, 2020	Federated Learning	—Unverified	0
iDLG: Improved Deep Leakage from Gradients	Jan 8, 2020	Federated Learningvalid	CodeCode Available	1

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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