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 3901–3925 of 6771 papers

Title	Date	Tasks	Status
Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations	Jun 6, 2023	Federated Learning	—Unverified
A Lightweight Method for Tackling Unknown Participation Statistics in Federated Averaging	Jun 6, 2023	Federated Learning	—Unverified
Unlocking the Potential of Federated Learning for Deeper Models	Jun 5, 2023	Federated Learning	—Unverified
Federated Deep Learning for Intrusion Detection in IoT Networks	Jun 5, 2023	Deep LearningFederated Learning	—Unverified
When Decentralized Optimization Meets Federated Learning	Jun 5, 2023	Federated Learning	—Unverified
Over-the-Air Federated Learning in Satellite systems	Jun 5, 2023	Earth ObservationFederated Learning	—Unverified
A Privacy-Preserving Federated Learning Approach for Kernel methods	Jun 5, 2023	Federated LearningPrivacy Preserving	—Unverified
Jammer classification with Federated Learning	Jun 5, 2023	ClassificationFederated Learning	—Unverified
Confidence-based federated distillation for vision-based lane-centering	Jun 5, 2023	Autonomous DrivingFederated Learning	—Unverified
Personalized Federated Domain Adaptation for Item-to-Item Recommendation	Jun 5, 2023	Domain AdaptationFederated Learning	—Unverified
Improving Accelerated Federated Learning with Compression and Importance Sampling	Jun 5, 2023	Federated Learning	—Unverified
Cross-Modal Vertical Federated Learning for MRI Reconstruction	Jun 5, 2023	DisentanglementFederated Learning	—Unverified
Riemannian Low-Rank Model Compression for Federated Learning with Over-the-Air Aggregation	Jun 4, 2023	Federated LearningModel Compression	—Unverified
Resilient Constrained Learning	Jun 4, 2023	FairnessFederated Learning	—Unverified
A Peer-to-peer Federated Continual Learning Network for Improving CT Imaging from Multiple Institutions	Jun 3, 2023	Computed Tomography (CT)Continual Learning	—Unverified
Over-the-Air Federated Learning In Broadband Communication	Jun 3, 2023	Federated LearningPrivacy Preserving	—Unverified
DU-Shapley: A Shapley Value Proxy for Efficient Dataset Valuation	Jun 3, 2023	Federated Learning	—Unverified
Federated Domain Generalization: A Survey	Jun 2, 2023	Domain AdaptationDomain Generalization	—Unverified
Decentralized Federated Learning: A Survey and Perspective	Jun 2, 2023	Federated LearningSurvey	—Unverified
Federated Learning Games for Reconfigurable Intelligent Surfaces via Causal Representations	Jun 2, 2023	Federated Learning	—Unverified
Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs	Jun 2, 2023	Federated LearningTime Series	CodeCode Available
On Knowledge Editing in Federated Learning: Perspectives, Challenges, and Future Directions	Jun 2, 2023	Federated Learningknowledge editing	—Unverified
Beta Thalassemia Carriers detection empowered federated Learning	Jun 2, 2023	Federated Learning	—Unverified
Covert Communication Based on the Poisoning Attack in Federated Learning	Jun 2, 2023	Computer SecurityFederated Learning	—Unverified
FACT: Federated Adversarial Cross Training	Jun 1, 2023	Domain AdaptationFederated Learning	CodeCode Available

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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