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 3626–3650 of 6771 papers

Title	Date	Tasks	Status	Hype
Unimodal Training-Multimodal Prediction: Cross-modal Federated Learning with Hierarchical Aggregation	Mar 27, 2023	DecoderFederated Learning	—Unverified	0
Asynchronous Online Federated Learning with Reduced Communication Requirements	Mar 27, 2023	Federated Learning	—Unverified	0
Adaptive Federated Learning via New Entropy Approach	Mar 27, 2023	Federated Learning	—Unverified	0
Neural Collapse Inspired Federated Learning with Non-iid Data	Mar 27, 2023	Federated Learning	—Unverified	0
CoDeC: Communication-Efficient Decentralized Continual Learning	Mar 27, 2023	Continual LearningFederated Learning	CodeCode Available	1
Personalized Federated Learning on Long-Tailed Data via Adversarial Feature Augmentation	Mar 27, 2023	Federated LearningPersonalized Federated Learning	CodeCode Available	0
The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning	Mar 27, 2023	Federated Learning	—Unverified	0
Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks	Mar 26, 2023	Edge-computingFederated Learning	—Unverified	0
Green Federated Learning	Mar 26, 2023	Federated Learning	—Unverified	0
Edge Selection and Clustering for Federated Learning in Optical Inter-LEO Satellite Constellation	Mar 25, 2023	ClusteringEarth Observation	—Unverified	0
Dealing With Heterogeneous 3D MR Knee Images: A Federated Few-Shot Learning Method With Dual Knowledge Distillation	Mar 25, 2023	Federated LearningFew-Shot Learning	CodeCode Available	0
A Survey on Model-based, Heuristic, and Machine Learning Optimization Approaches in RIS-aided Wireless Networks	Mar 25, 2023	Federated LearningGraph Learning	—Unverified	0
Federated Learning without Full Labels: A Survey	Mar 25, 2023	Federated LearningSelf-Supervised Learning	—Unverified	0
A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computing	Mar 24, 2023	Distributed ComputingFederated Learning	—Unverified	0
Secure Federated Learning for Cognitive Radio Sensing	Mar 23, 2023	Federated Learning	—Unverified	0
Federated Learning for Metaverse: A Survey	Mar 23, 2023	Edge-computingFederated Learning	—Unverified	0
AI Models Close to your Chest: Robust Federated Learning Strategies for Multi-site CT	Mar 23, 2023	Federated Learning	—Unverified	0
Federated Uncertainty-Aware Aggregation for Fundus Diabetic Retinopathy Staging	Mar 23, 2023	Federated Learning	—Unverified	0
Automated Federated Learning in Mobile Edge Networks -- Fast Adaptation and Convergence	Mar 23, 2023	Federated LearningMeta-Learning	—Unverified	0
Failure-tolerant Distributed Learning for Anomaly Detection in Wireless Networks	Mar 23, 2023	Anomaly DetectionFederated Learning	—Unverified	0
FedGH: Heterogeneous Federated Learning with Generalized Global Header	Mar 23, 2023	Federated LearningPrediction	CodeCode Available	2
FS-Real: Towards Real-World Cross-Device Federated Learning	Mar 23, 2023	Federated Learning	—Unverified	0
Re-thinking Federated Active Learning based on Inter-class Diversity	Mar 22, 2023	Active LearningDiversity	CodeCode Available	1
Prototype Helps Federated Learning: Towards Faster Convergence	Mar 22, 2023	Federated Learning	—Unverified	0
Delay-Aware Hierarchical Federated Learning	Mar 22, 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