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 5726–5750 of 6771 papers

Title	Date	Tasks	Status	Hype
Federated Learning for Short-term Residential Load Forecasting	May 27, 2021	Computational EfficiencyFederated Learning	—Unverified	0
Clustered Federated Learning via Generalized Total Variation Minimization	May 26, 2021	Distributed ComputingEdge-computing	CodeCode Available	0
Federated Meta Learning Enhanced Acoustic Radio Cooperative Framework for Ocean of Things Underwater Acoustic Communications	May 24, 2021	ARCFederated Learning	—Unverified	0
Fed-NILM: A Federated Learning-based Non-Intrusive Load Monitoring Method for Privacy-Protection	May 24, 2021	Federated LearningNon-Intrusive Load Monitoring	—Unverified	0
Federated Graph Learning -- A Position Paper	May 24, 2021	Federated LearningGraph Learning	—Unverified	0
FedScale: Benchmarking Model and System Performance of Federated Learning at Scale	May 24, 2021	BenchmarkingFederated Learning	CodeCode Available	1
Fast Federated Learning by Balancing Communication Trade-Offs	May 23, 2021	Federated LearningPrivacy Preserving	CodeCode Available	1
DoStoVoQ: Doubly Stochastic Voronoi Vector Quantization SGD for Federated Learning	May 21, 2021	Federated LearningQuantization	—Unverified	0
Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis	May 21, 2021	COVID-19 DiagnosisFederated Learning	—Unverified	0
HyFed: A Hybrid Federated Framework for Privacy-preserving Machine Learning	May 21, 2021	BIG-bench Machine LearningFederated Learning	CodeCode Available	1
Data-Free Knowledge Distillation for Heterogeneous Federated Learning	May 20, 2021	Data-free Knowledge DistillationFederated Learning	CodeCode Available	1
Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learning	May 20, 2021	BIG-bench Machine LearningFederated Learning	—Unverified	0
User-Level Label Leakage from Gradients in Federated Learning	May 19, 2021	Federated Learning	CodeCode Available	0
A Privacy-Preserving Approach to Extraction of Personal Information through Automatic Annotation and Federated Learning	May 19, 2021	Federated LearningNER	CodeCode Available	0
Separation of Powers in Federated Learning	May 19, 2021	Federated Learning	—Unverified	0
Prototype Guided Federated Learning of Visual Feature Representations	May 19, 2021	Federated Learningimage-classification	—Unverified	0
DRIVE: One-bit Distributed Mean Estimation	May 18, 2021	Federated Learning	CodeCode Available	0
DID-eFed: Facilitating Federated Learning as a Service with Decentralized Identities	May 18, 2021	Federated LearningManagement	—Unverified	0
EasyFL: A Low-code Federated Learning Platform For Dummies	May 17, 2021	Federated LearningPrivacy Preserving	CodeCode Available	1
Towards Unsupervised Domain Adaptation for Deep Face Recognition under Privacy Constraints via Federated Learning	May 17, 2021	ClusteringDomain Adaptation	—Unverified	0
Privacy-Preserving Constrained Domain Generalization via Gradient Alignment	May 14, 2021	ClassificationDomain Generalization	—Unverified	0
Node Selection Toward Faster Convergence for Federated Learning on Non-IID Data	May 14, 2021	Federated Learning	CodeCode Available	1
OpenFL: An open-source framework for Federated Learning	May 13, 2021	Federated Learning	—Unverified	0
The FeatureCloud AI Store for Federated Learning in Biomedicine and Beyond	May 12, 2021	Federated LearningPrivacy Preserving	CodeCode Available	0
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning	May 12, 2021	ClusteringFederated Learning	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