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 5901–5925 of 6771 papers

Title	Date	Tasks	Status
Understanding Unintended Memorization in Language Models Under Federated Learning	Jun 1, 2021	Federated LearningMemorization	—Unverified
An Investigation towards Differentially Private Sequence Tagging in a Federated Framework	Jun 1, 2021	Federated Learningnamed-entity-recognition	—Unverified
QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learning	Jun 1, 2021	Federated Learning	—Unverified
Unifying Distillation with Personalization in Federated Learning	May 31, 2021	FairnessFederated Learning	—Unverified
Towards a Federated Learning Framework for Heterogeneous Devices of Internet of Things	May 31, 2021	Federated Learning	—Unverified
Federated Learning for Industrial Internet of Things in Future Industries	May 31, 2021	Federated Learning	—Unverified
CDMA: A Practical Cross-Device Federated Learning Algorithm for General Minimax Problems	May 29, 2021	Federated LearningGenerative Adversarial Network	CodeCode Available
Concept drift detection and adaptation for federated and continual learning	May 27, 2021	Continual LearningDrift Detection	—Unverified
Federated Learning for Short-term Residential Load Forecasting	May 27, 2021	Computational EfficiencyFederated Learning	—Unverified
Clustered Federated Learning via Generalized Total Variation Minimization	May 26, 2021	Distributed ComputingEdge-computing	CodeCode Available
Federated Graph Learning -- A Position Paper	May 24, 2021	Federated LearningGraph Learning	—Unverified
Federated Meta Learning Enhanced Acoustic Radio Cooperative Framework for Ocean of Things Underwater Acoustic Communications	May 24, 2021	ARCFederated Learning	—Unverified
Fed-NILM: A Federated Learning-based Non-Intrusive Load Monitoring Method for Privacy-Protection	May 24, 2021	Federated LearningNon-Intrusive Load Monitoring	—Unverified
Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis	May 21, 2021	COVID-19 DiagnosisFederated Learning	—Unverified
DoStoVoQ: Doubly Stochastic Voronoi Vector Quantization SGD for Federated Learning	May 21, 2021	Federated LearningQuantization	—Unverified
Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learning	May 20, 2021	BIG-bench Machine LearningFederated Learning	—Unverified
Prototype Guided Federated Learning of Visual Feature Representations	May 19, 2021	Federated Learningimage-classification	—Unverified
User-Level Label Leakage from Gradients in Federated Learning	May 19, 2021	Federated Learning	CodeCode Available
A Privacy-Preserving Approach to Extraction of Personal Information through Automatic Annotation and Federated Learning	May 19, 2021	Federated LearningNER	CodeCode Available
Separation of Powers in Federated Learning	May 19, 2021	Federated Learning	—Unverified
DID-eFed: Facilitating Federated Learning as a Service with Decentralized Identities	May 18, 2021	Federated LearningManagement	—Unverified
DRIVE: One-bit Distributed Mean Estimation	May 18, 2021	Federated Learning	CodeCode Available
Towards Unsupervised Domain Adaptation for Deep Face Recognition under Privacy Constraints via Federated Learning	May 17, 2021	ClusteringDomain Adaptation	—Unverified
Privacy-Preserving Constrained Domain Generalization via Gradient Alignment	May 14, 2021	ClassificationDomain Generalization	—Unverified
OpenFL: An open-source framework for Federated Learning	May 13, 2021	Federated 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