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 3826–3850 of 6771 papers

Title	Date	Tasks	Status	Hype
Federated Privacy-preserving Collaborative Filtering for On-Device Next App Prediction	Feb 5, 2023	Collaborative FilteringFederated Learning	—Unverified	0
Use of Federated Learning and Blockchain towards Securing Financial Services	Feb 4, 2023	Federated Learning	—Unverified	0
Digital Over-the-Air Federated Learning in Multi-Antenna Systems	Feb 4, 2023	Federated Learning	—Unverified	0
GAN-based Vertical Federated Learning for Label Protection in Binary Classification	Feb 4, 2023	Binary ClassificationFederated Learning	—Unverified	0
FedSpectral+: Spectral Clustering using Federated Learning	Feb 4, 2023	ClusteringFederated Learning	—Unverified	0
Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning	Feb 4, 2023	Federated LearningGraph Embedding	—Unverified	0
Vertical Federated Learning: Taxonomies, Threats, and Prospects	Feb 3, 2023	Federated LearningVertical Federated Learning	—Unverified	0
Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks	Feb 3, 2023	Backdoor AttackFederated Learning	—Unverified	0
Convergence Analysis of Sequential Split Learning on Heterogeneous Data	Feb 3, 2023	Federated Learning	—Unverified	0
GTV: Generating Tabular Data via Vertical Federated Learning	Feb 3, 2023	Federated LearningPrivacy Preserving	—Unverified	0
Instance-wise Batch Label Restoration via Gradients in Federated Learning	Feb 2, 2023	Federated Learning	CodeCode Available	0
Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy	Feb 2, 2023	Federated Learning	—Unverified	0
On the Efficacy of Differentially Private Few-shot Image Classification	Feb 2, 2023	Federated LearningFew-Shot Image Classification	CodeCode Available	0
No One Left Behind: Real-World Federated Class-Incremental Learning	Feb 2, 2023	class-incremental learningClass Incremental Learning	CodeCode Available	1
Federated Analytics: A survey	Feb 2, 2023	Federated LearningPrivacy Preserving	—Unverified	0
FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations	Feb 2, 2023	Federated Learningglobal-optimization	CodeCode Available	0
Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection	Feb 1, 2023	Autonomous DrivingFederated Learning	CodeCode Available	1
DoCoFL: Downlink Compression for Cross-Device Federated Learning	Feb 1, 2023	Federated Learning	—Unverified	0
Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-supervised Learning Approach	Feb 1, 2023	Federated LearningSelf-Supervised Learning	—Unverified	0
FLSTRA: Federated Learning in Stratosphere	Feb 1, 2023	Federated Learning	—Unverified	0
CATFL: Certificateless Authentication-based Trustworthy Federated Learning for 6G Semantic Communications	Feb 1, 2023	Data PoisoningDecoder	—Unverified	0
Distributed sequential federated learning	Jan 31, 2023	Distributed ComputingFederated Learning	—Unverified	0
Truthful Incentive Mechanism for Federated Learning with Crowdsourced Data Labeling	Jan 31, 2023	Federated Learning	—Unverified	0
The Fair Value of Data Under Heterogeneous Privacy Constraints in Federated Learning	Jan 30, 2023	FairnessFederated Learning	—Unverified	0
FedFA: Federated Feature Augmentation	Jan 30, 2023	Federated Learning	CodeCode Available	1

Show:10 25 50

← PrevPage 154 of 271Next →

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