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
Faster Non-Convex Federated Learning via Global and Local Momentum	Dec 7, 2020	Federated Learning	—Unverified
Design and Analysis of Uplink and Downlink Communications for Federated Learning	Dec 7, 2020	Federated LearningQuantization	—Unverified
Dynamic Clustering in Federated Learning	Dec 7, 2020	ClusteringFederated Learning	—Unverified
TornadoAggregate: Accurate and Scalable Federated Learning via the Ring-Based Architecture	Dec 6, 2020	Federated Learning	—Unverified
Probabilistic Federated Learning of Neural Networks Incorporated with Global Posterior Information	Dec 6, 2020	Federated Learning	—Unverified
SoK: Training Machine Learning Models over Multiple Sources with Privacy Preservation	Dec 6, 2020	BIG-bench Machine LearningFederated Learning	—Unverified
FedSiam: Towards Adaptive Federated Semi-Supervised Learning	Dec 6, 2020	Federated Learning	—Unverified
Accurate and Fast Federated Learning via Combinatorial Multi-Armed Bandits	Dec 6, 2020	BIG-bench Machine LearningFederated Learning	—Unverified
Mitigating Bias in Federated Learning	Dec 4, 2020	FairnessFederated Learning	—Unverified
Federated Learning with Heterogeneous Labels and Models for Mobile Activity Monitoring	Dec 4, 2020	Activity RecognitionBIG-bench Machine Learning	—Unverified
FAT: Federated Adversarial Training	Dec 3, 2020	Adversarial RobustnessFederated Learning	—Unverified
Federated Learning for Personalized Humor Recognition	Dec 3, 2020	DiversityFederated Learning	—Unverified
Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications	Dec 2, 2020	BIG-bench Machine LearningCloud Computing	—Unverified
Blockchain Assisted Decentralized Federated Learning (BLADE-FL) with Lazy Clients	Dec 2, 2020	Federated Learning	—Unverified
Second-Order Guarantees in Federated Learning	Dec 2, 2020	Deep LearningFederated Learning	—Unverified
MYSTIKO : : Cloud-Mediated, Private, Federated Gradient Descent	Dec 1, 2020	Federated Learning	—Unverified
A Systematic Literature Review on Federated Learning: From A Model Quality Perspective	Dec 1, 2020	ArticlesFederated Learning	—Unverified
Federated Marginal Personalization for ASR Rescoring	Dec 1, 2020	Federated Learningspeech-recognition	—Unverified
Federated Learning for Spoken Language Understanding	Dec 1, 2020	Federated LearningIntent Detection	—Unverified
Communication-Efficient Federated Distillation	Dec 1, 2020	Federated Learningimage-classification	—Unverified
Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach	Dec 1, 2020	Federated LearningMeta-Learning	—Unverified
Fast-Convergent Federated Learning with Adaptive Weighting	Dec 1, 2020	Federated Learning	—Unverified
Edge-assisted Democratized Learning Towards Federated Analytics	Dec 1, 2020	Distributed ComputingEdge-computing	—Unverified
Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management	Nov 28, 2020	Distributed ComputingFederated Learning	—Unverified
LocKedge: Low-Complexity Cyberattack Detection in IoT Edge Computing	Nov 28, 2020	Edge-computingFederated Learning	—Unverified

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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