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 6226–6250 of 6771 papers

Title	Date	Tasks	Status	Hype
FLAME: Differentially Private Federated Learning in the Shuffle Model	Sep 17, 2020	Federated Learning	CodeCode Available	1
Distilled One-Shot Federated Learning	Sep 17, 2020	Dataset DistillationFederated Learning	—Unverified	0
Byzantine-Robust Variance-Reduced Federated Learning over Distributed Non-i.i.d. Data	Sep 17, 2020	Federated Learning	CodeCode Available	0
FedSmart: An Auto Updating Federated Learning Optimization Mechanism	Sep 16, 2020	Federated LearningPrivacy Preserving	—Unverified	0
Federated Dynamic GNN with Secure Aggregation	Sep 15, 2020	Federated LearningGraph Neural Network	—Unverified	0
A Vertical Federated Learning Method for Interpretable Scorecard and Its Application in Credit Scoring	Sep 14, 2020	Federated LearningManagement	—Unverified	0
Effective Federated Adaptive Gradient Methods with Non-IID Decentralized Data	Sep 14, 2020	Edge-computingFederated Learning	—Unverified	0
Robustness and Personalization in Federated Learning: A Unified Approach via Regularization	Sep 14, 2020	Federated LearningPersonalized Federated Learning	—Unverified	0
A Principled Approach to Data Valuation for Federated Learning	Sep 14, 2020	Data SummarizationData Valuation	—Unverified	0
Private data sharing between decentralized users through the privGAN architecture	Sep 14, 2020	Federated Learning	—Unverified	0
SAPAG: A Self-Adaptive Privacy Attack From Gradients	Sep 14, 2020	Federated LearningReconstruction Attack	—Unverified	0
FLaPS: Federated Learning and Privately Scaling	Sep 13, 2020	Federated Learning	CodeCode Available	0
Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient Descent	Sep 11, 2020	Bayesian InferenceFederated Learning	CodeCode Available	1
Trading Data For Learning: Incentive Mechanism For On-Device Federated Learning	Sep 11, 2020	Federated Learning	—Unverified	0
Federated Model Distillation with Noise-Free Differential Privacy	Sep 11, 2020	Federated Learning	—Unverified	0
Low-Rank Training of Deep Neural Networks for Emerging Memory Technology	Sep 8, 2020	Computational EfficiencyDecision Making	—Unverified	0
FedCM: A Real-time Contribution Measurement Method for Participants in Federated Learning	Sep 8, 2020	Federated Learning	—Unverified	0
Local and Central Differential Privacy for Robustness and Privacy in Federated Learning	Sep 8, 2020	Federated Learning	—Unverified	0
Hybrid Differentially Private Federated Learning on Vertically Partitioned Data	Sep 6, 2020	Federated LearningPrivacy Preserving	—Unverified	0
Blockchain-based Federated Learning for Failure Detection in Industrial IoT	Sep 6, 2020	Federated LearningPrivacy Preserving	—Unverified	0
Particle Swarm Optimized Federated Learning For Industrial IoT and Smart City Services	Sep 5, 2020	Federated Learningglobal-optimization	—Unverified	0
FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning	Sep 4, 2020	Bayesian InferenceFederated Learning	CodeCode Available	1
ESMFL: Efficient and Secure Models for Federated Learning	Sep 3, 2020	Federated LearningPrivacy Preserving	—Unverified	0
Federated Learning for Breast Density Classification: A Real-World Implementation	Sep 3, 2020	breast density classificationClassification	—Unverified	0
Fed-Sim: Federated Simulation for Medical Imaging	Sep 1, 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