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 3526–3550 of 6771 papers

Title	Date	Tasks	Status	Hype
Federated Learning with Uncertainty-Based Client Clustering for Fleet-Wide Fault Diagnosis	Apr 26, 2023	ClusteringFault Diagnosis	CodeCode Available	0
Secure Communication Model For Quantum Federated Learning: A Post Quantum Cryptography (PQC) Framework	Apr 26, 2023	Federated Learning	CodeCode Available	0
FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models	Apr 26, 2023	Federated LearningPrivacy Preserving	—Unverified	0
Bayesian Federated Learning: A Survey	Apr 26, 2023	Federated LearningPrivacy Preserving	—Unverified	0
Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM	Apr 25, 2023	Federated LearningPersonalized Federated Learning	—Unverified	0
User-Centric Federated Learning: Trading off Wireless Resources for Personalization	Apr 25, 2023	Federated LearningPrivacy Preserving	—Unverified	0
Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning	Apr 25, 2023	Contrastive LearningFederated Learning	CodeCode Available	1
Blockchain-based Federated Learning with Secure Aggregation in Trusted Execution Environment for Internet-of-Things	Apr 25, 2023	Federated Learning	—Unverified	0
Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications	Apr 24, 2023	Federated Learning	CodeCode Available	1
More Communication Does Not Result in Smaller Generalization Error in Federated Learning	Apr 24, 2023	Federated Learning	—Unverified	0
FedPIDAvg: A PID controller inspired aggregation method for Federated Learning	Apr 24, 2023	Federated LearningTumor Segmentation	—Unverified	0
Multi-Source to Multi-Target Decentralized Federated Domain Adaptation	Apr 24, 2023	Domain AdaptationFederated Learning	—Unverified	0
Personalized Federated Learning via Gradient Modulation for Heterogeneous Text Summarization	Apr 23, 2023	Federated LearningPersonalized Federated Learning	—Unverified	0
Universal Adversarial Backdoor Attacks to Fool Vertical Federated Learning in Cloud-Edge Collaboration	Apr 22, 2023	Binary ClassificationFederated Learning	—Unverified	0
Breaching FedMD: Image Recovery via Paired-Logits Inversion Attack	Apr 22, 2023	Federated Learning	CodeCode Available	0
Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning	Apr 21, 2023	Federated LearningModel Poisoning	—Unverified	0
Joint Client Assignment and UAV Route Planning for Indirect-Communication Federated Learning	Apr 21, 2023	Federated Learning	—Unverified	0
Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applications	Apr 21, 2023	Federated Learning	—Unverified	0
Get Rid Of Your Trail: Remotely Erasing Backdoors in Federated Learning	Apr 20, 2023	Federated Learningimage-classification	—Unverified	0
Federated Compositional Deep AUC Maximization	Apr 20, 2023	Federated Learning	—Unverified	0
Model Pruning Enables Localized and Efficient Federated Learning for Yield Forecasting and Data Sharing	Apr 19, 2023	Federated LearningNetwork Pruning	—Unverified	0
Federated Alternate Training (FAT): Leveraging Unannotated Data Silos in Federated Segmentation for Medical Imaging	Apr 18, 2023	Federated LearningSegmentation	—Unverified	0
BadVFL: Backdoor Attacks in Vertical Federated Learning	Apr 18, 2023	Backdoor AttackFederated Learning	—Unverified	0
Learning to Transmit with Provable Guarantees in Wireless Federated Learning	Apr 18, 2023	Federated Learning	CodeCode Available	1
Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networks	Apr 18, 2023	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