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 3651–3675 of 6771 papers

Title	Date	Tasks	Status	Hype
LOKI: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulation	Mar 21, 2023	Federated LearningReconstruction Attack	CodeCode Available	0
A Survey on Class Imbalance in Federated Learning	Mar 21, 2023	Federated LearningSurvey	—Unverified	0
STDLens: Model Hijacking-Resilient Federated Learning for Object Detection	Mar 21, 2023	Federated Learningobject-detection	CodeCode Available	0
Poisoning Attacks in Federated Edge Learning for Digital Twin 6G-enabled IoTs: An Anticipatory Study	Mar 21, 2023	Federated LearningPrivacy Preserving	—Unverified	0
FedMAE: Federated Self-Supervised Learning with One-Block Masked Auto-Encoder	Mar 20, 2023	Federated LearningImage Reconstruction	—Unverified	0
Recursive Euclidean Distance Based Robust Aggregation Technique For Federated Learning	Mar 20, 2023	Data PoisoningFederated Learning	—Unverified	0
Make Landscape Flatter in Differentially Private Federated Learning	Mar 20, 2023	Federated Learning	CodeCode Available	1
FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System	Mar 20, 2023	Federated LearningPrivacy Preserving	CodeCode Available	4
On the Convergence of Decentralized Federated Learning Under Imperfect Information Sharing	Mar 19, 2023	Distributed OptimizationFederated Learning	CodeCode Available	0
A Survey of Federated Learning for Connected and Automated Vehicles	Mar 19, 2023	Federated LearningMotion Planning	—Unverified	0
Experimenting with Normalization Layers in Federated Learning on non-IID scenarios	Mar 19, 2023	Federated LearningPrivacy Preserving	CodeCode Available	0
PFSL: Personalized & Fair Split Learning with Data & Label Privacy for thin clients	Mar 19, 2023	FairnessFederated Learning	CodeCode Available	1
Hierarchical Personalized Federated Learning Over Massive Mobile Edge Computing Networks	Mar 19, 2023	Edge-computingFederated Learning	—Unverified	0
Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach	Mar 18, 2023	Federated LearningScheduling	—Unverified	0
Byzantine-Resilient Federated Learning at Edge	Mar 18, 2023	Federated Learning	—Unverified	0
FedRight: An Effective Model Copyright Protection for Federated Learning	Mar 18, 2023	Federated Learning	—Unverified	0
Report of the Medical Image De-Identification (MIDI) Task Group -- Best Practices and Recommendations	Mar 18, 2023	De-identificationFederated Learning	—Unverified	0
No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifier	Mar 17, 2023	Classifier calibrationFederated Learning	CodeCode Available	1
An Empirical Evaluation of Federated Contextual Bandit Algorithms	Mar 17, 2023	Federated LearningMulti-Armed Bandits	—Unverified	0
GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Data	Mar 16, 2023	Federated LearningGraph Neural Network	CodeCode Available	0
Towards Cooperative Federated Learning over Heterogeneous Edge/Fog Networks	Mar 15, 2023	Federated Learning	—Unverified	0
Efficient and Secure Federated Learning for Financial Applications	Mar 15, 2023	Federated Learning	—Unverified	0
Optimization Design for Federated Learning in Heterogeneous 6G Networks	Mar 15, 2023	Federated LearningManagement	—Unverified	0
Visual Prompt Based Personalized Federated Learning	Mar 15, 2023	Federated Learningimage-classification	—Unverified	0
Comparative Evaluation of Data Decoupling Techniques for Federated Machine Learning with Database as a Service	Mar 15, 2023	Federated LearningManagement	—Unverified	0

Show:10 25 50

← PrevPage 147 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