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 3301–3325 of 6771 papers

Title	Date	Tasks	Status	Hype
Data-Heterogeneous Hierarchical Federated Learning with Mobility	Jun 19, 2023	Federated LearningPrivacy Preserving	—Unverified	0
FSAR: Federated Skeleton-based Action Recognition with Adaptive Topology Structure and Knowledge Distillation	Jun 19, 2023	Action RecognitionFederated Learning	—Unverified	0
Blockchain-Enabled Federated Learning: A Reference Architecture Design, Implementation, and Verification	Jun 19, 2023	Federated Learning	—Unverified	0
Edge Intelligence Over the Air: Two Faces of Interference in Federated Learning	Jun 17, 2023	Federated Learning	—Unverified	0
Federated Learning Based Distributed Localization of False Data Injection Attacks on Smart Grids	Jun 17, 2023	Federated Learning	—Unverified	0
Federated Few-shot Learning	Jun 17, 2023	ArticlesFederated Learning	CodeCode Available	1
Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated Graph Neural Network	Jun 17, 2023	Backdoor AttackFederated Learning	CodeCode Available	1
Towards Quantum Federated Learning	Jun 16, 2023	Federated Learning	—Unverified	0
Fedstellar: A Platform for Decentralized Federated Learning	Jun 16, 2023	Federated Learning	CodeCode Available	1
Continual Adaptation of Vision Transformers for Federated Learning	Jun 16, 2023	Federated Learningimage-classification	CodeCode Available	0
FedMultimodal: A Benchmark For Multimodal Federated Learning	Jun 15, 2023	Emotion RecognitionFederated Learning	CodeCode Available	0
Towards Practical Federated Causal Structure Learning	Jun 15, 2023	Federated Learningscientific discovery	CodeCode Available	0
Inferring Communities of Interest in Collaborative Learning-based Recommender Systems	Jun 15, 2023	Community DetectionFederated Learning	—Unverified	0
Privacy Risks in Reinforcement Learning for Household Robots	Jun 15, 2023	Decision MakingFederated Learning	—Unverified	0
An Efficient and Multi-private Key Secure Aggregation for Federated Learning	Jun 15, 2023	Federated Learning	—Unverified	0
A Simple Data Augmentation for Feature Distribution Skewed Federated Learning	Jun 14, 2023	Data AugmentationFederated Learning	—Unverified	0
A Client-server Deep Federated Learning for Cross-domain Surgical Image Segmentation	Jun 14, 2023	DiagnosticDomain Adaptation	CodeCode Available	0
Differentially Private Wireless Federated Learning Using Orthogonal Sequences	Jun 14, 2023	Federated LearningPrivacy Preserving	—Unverified	0
Federated Learning-based Vehicle Trajectory Prediction against Cyberattacks	Jun 14, 2023	Federated LearningPrediction	CodeCode Available	1
Provably Personalized and Robust Federated Learning	Jun 14, 2023	ClusteringFederated Learning	CodeCode Available	0
Learning to Specialize: Joint Gating-Expert Training for Adaptive MoEs in Decentralized Settings	Jun 14, 2023	DiversityFederated Learning	—Unverified	0
Privacy Inference-Empowered Stealthy Backdoor Attack on Federated Learning under Non-IID Scenarios	Jun 13, 2023	Backdoor AttackFederated Learning	—Unverified	0
(Amplified) Banded Matrix Factorization: A unified approach to private training	Jun 13, 2023	Federated Learning	—Unverified	0
Privacy Preserving Bayesian Federated Learning in Heterogeneous Settings	Jun 13, 2023	Federated LearningPrivacy Preserving	—Unverified	0
Temporal Gradient Inversion Attacks with Robust Optimization	Jun 13, 2023	Federated LearningPrivacy Preserving	—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