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 3376–3400 of 6771 papers

Title	Date	Tasks	Status
Poisoning Semi-supervised Federated Learning via Unlabeled Data: Attacks and Defenses	Dec 8, 2020	Federated LearningQuantization	—Unverified
Real-time End-to-End Federated Learning: An Automotive Case Study	Mar 22, 2021	Autonomous DrivingBIG-bench Machine Learning	—Unverified
Real-time Federated Evolutionary Neural Architecture Search	Mar 4, 2020	BIG-bench Machine LearningFederated Learning	—Unverified
Real-World Federated Learning in Radiology: Hurdles to overcome and Benefits to gain	May 15, 2024	Federated Learning	—Unverified
Recent Advances on Federated Learning: A Systematic Survey	Jan 3, 2023	Federated LearningPrivacy Preserving	—Unverified
Recent Methodological Advances in Federated Learning for Healthcare	Oct 4, 2023	Federated Learning	—Unverified
Recommender Engine Driven Client Selection in Federated Brain Tumor Segmentation	Dec 28, 2024	Brain Tumor SegmentationCollaborative Filtering	—Unverified
Reconciling Hessian-Informed Acceleration and Scalar-Only Communication for Efficient Federated Zeroth-Order Fine-Tuning	Jun 3, 2025	Federated Learning	—Unverified
Reconstructing Training Data from Model Gradient, Provably	Dec 7, 2022	Federated Learningmodel	—Unverified
Recovering Global Data Distribution Locally in Federated Learning	Sep 21, 2024	Federated Learningimage-classification	—Unverified
Recovering Labels from Local Updates in Federated Learning	May 2, 2024	Federated Learning	—Unverified
RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense	Apr 11, 2023	Adversarial AttackAttribute	—Unverified
Recursive Euclidean Distance Based Robust Aggregation Technique For Federated Learning	Mar 20, 2023	Data PoisoningFederated Learning	—Unverified
Redefining Clustered Federated Learning for System Identification: The Path of ClusterCraft	May 22, 2025	ClusteringFederated Learning	—Unverified
FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learning	Aug 25, 2022	Backdoor AttackData Poisoning	—Unverified
Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversary	Jan 18, 2021	Federated LearningLesion Classification	—Unverified
Federated Class-Incremental Learning with Hierarchical Generative Prototypes	Jun 4, 2024	class-incremental learningClass Incremental Learning	—Unverified
Reducing Communication for Split Learning by Randomized Top-k Sparsification	May 29, 2023	Federated LearningQuantization	—Unverified
Reducing Communication Overhead in Federated Learning for Network Anomaly Detection with Adaptive Client Selection	Mar 19, 2025	Anomaly DetectionFederated Learning	—Unverified
Reducing Impacts of System Heterogeneity in Federated Learning using Weight Update Magnitudes	Aug 30, 2022	Federated Learning	—Unverified
Reducing Spurious Correlation for Federated Domain Generalization	Jul 27, 2024	Domain GeneralizationFederated Learning	—Unverified
FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learning	Aug 16, 2021	Federated LearningImage Classification	—Unverified
Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning	Dec 5, 2022	Federated LearningSemantic Similarity	—Unverified
ReFRS: Resource-efficient Federated Recommender System for Dynamic and Diversified User Preferences	Jul 28, 2022	Federated LearningRecommendation Systems	—Unverified
Regulating Clients' Noise Adding in Federated Learning without Verification	Feb 24, 2023	Federated Learning	—Unverified

Show:10 25 50

← PrevPage 136 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