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 6076–6100 of 6771 papers

Title	Date	Tasks	Status	Hype
Poisoning Semi-supervised Federated Learning via Unlabeled Data: Attacks and Defenses	Dec 8, 2020	Federated LearningQuantization	—Unverified	0
GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs	Dec 8, 2020	Federated LearningGeneral Classification	—Unverified	0
Privacy and Robustness in Federated Learning: Attacks and Defenses	Dec 7, 2020	Federated LearningPrivacy Preserving	—Unverified	0
Faster Non-Convex Federated Learning via Global and Local Momentum	Dec 7, 2020	Federated Learning	—Unverified	0
Dynamic Clustering in Federated Learning	Dec 7, 2020	ClusteringFederated Learning	—Unverified	0
Design and Analysis of Uplink and Downlink Communications for Federated Learning	Dec 7, 2020	Federated LearningQuantization	—Unverified	0
TornadoAggregate: Accurate and Scalable Federated Learning via the Ring-Based Architecture	Dec 6, 2020	Federated Learning	—Unverified	0
SoK: Training Machine Learning Models over Multiple Sources with Privacy Preservation	Dec 6, 2020	BIG-bench Machine LearningFederated Learning	—Unverified	0
Accurate and Fast Federated Learning via Combinatorial Multi-Armed Bandits	Dec 6, 2020	BIG-bench Machine LearningFederated Learning	—Unverified	0
FedSiam: Towards Adaptive Federated Semi-Supervised Learning	Dec 6, 2020	Federated Learning	—Unverified	0
Probabilistic Federated Learning of Neural Networks Incorporated with Global Posterior Information	Dec 6, 2020	Federated Learning	—Unverified	0
Unleashing the Tiger: Inference Attacks on Split Learning	Dec 4, 2020	Federated Learning	CodeCode Available	1
Federated Learning with Heterogeneous Labels and Models for Mobile Activity Monitoring	Dec 4, 2020	Activity RecognitionBIG-bench Machine Learning	—Unverified	0
Mitigating Bias in Federated Learning	Dec 4, 2020	FairnessFederated Learning	—Unverified	0
FAT: Federated Adversarial Training	Dec 3, 2020	Adversarial RobustnessFederated Learning	—Unverified	0
Federated Learning for Personalized Humor Recognition	Dec 3, 2020	DiversityFederated Learning	—Unverified	0
Robust Federated Learning with Noisy Labels	Dec 3, 2020	Federated LearningLearning with noisy labels	CodeCode Available	1
Blockchain Assisted Decentralized Federated Learning (BLADE-FL) with Lazy Clients	Dec 2, 2020	Federated Learning	—Unverified	0
Second-Order Guarantees in Federated Learning	Dec 2, 2020	Deep LearningFederated Learning	—Unverified	0
Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications	Dec 2, 2020	BIG-bench Machine LearningCloud Computing	—Unverified	0
Federated Learning for Spoken Language Understanding	Dec 1, 2020	Federated LearningIntent Detection	—Unverified	0
A Systematic Literature Review on Federated Learning: From A Model Quality Perspective	Dec 1, 2020	ArticlesFederated Learning	—Unverified	0
Federated Marginal Personalization for ASR Rescoring	Dec 1, 2020	Federated Learningspeech-recognition	—Unverified	0
Communication-Efficient Federated Distillation	Dec 1, 2020	Federated Learningimage-classification	—Unverified	0
MYSTIKO : : Cloud-Mediated, Private, Federated Gradient Descent	Dec 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