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 4401–4425 of 6771 papers

Title	Date	Tasks	Status
FedSAF: A Federated Learning Framework for Enhanced Gastric Cancer Detection and Privacy Preservation	Mar 20, 2025	Federated Learning	—Unverified
Fed-Safe: Securing Federated Learning in Healthcare Against Adversarial Attacks	Oct 12, 2023	Federated LearningMedical Image Analysis	—Unverified
FedSampling: A Better Sampling Strategy for Federated Learning	Jun 25, 2023	Federated LearningPrivacy Preserving	—Unverified
FedSat: A Statistical Aggregation Approach for Class Imbalanced Clients in Federated Learning	Jul 4, 2024	Federated LearningPrivacy Preserving	—Unverified
FedSAUC: A Similarity-Aware Update Control for Communication-Efficient Federated Learning in Edge Computing	Apr 7, 2025	Edge-computingFederated Learning	—Unverified
FedSCA: Federated Tuning with Similarity-guided Collaborative Aggregation for Heterogeneous Medical Image Segmentation	Mar 19, 2025	Computational EfficiencyFederated Learning	—Unverified
FedScalar: A Communication efficient Federated Learning	Oct 3, 2024	Federated Learning	—Unverified
FedSC: Provable Federated Self-supervised Learning with Spectral Contrastive Objective over Non-i.i.d. Data	May 7, 2024	Federated LearningSelf-Supervised Learning	—Unverified
FedSDD: Scalable and Diversity-enhanced Distillation for Model Aggregation in Federated Learning	Dec 28, 2023	DiversityFederated Learning	—Unverified
FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learning	Feb 21, 2023	Feature Importancefeature selection	—Unverified
FedSEAL: Semi-Supervised Federated Learning with Self-Ensemble Learning and Negative Learning	Oct 15, 2021	Ensemble LearningFederated Learning	—Unverified
FedSeg: Class-Heterogeneous Federated Learning for Semantic Segmentation	Jan 1, 2023	Contrastive LearningFederated Learning	—Unverified
FedSelect: Customized Selection of Parameters for Fine-Tuning during Personalized Federated Learning	Jun 23, 2023	Federated LearningPersonalized Federated Learning	—Unverified
FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection	Mar 24, 2020	Federated Learning	—Unverified
FedSiam: Towards Adaptive Federated Semi-Supervised Learning	Dec 6, 2020	Federated Learning	—Unverified
FedSheafHN: Personalized Federated Learning on Graph-structured Data	May 25, 2024	Federated LearningGraph Embedding	—Unverified
FedShift: Tackling Dual Heterogeneity Problem of Federated Learning via Weight Shift Aggregation	Feb 2, 2024	DiversityFederated Learning	—Unverified
FedShuffle: Recipes for Better Use of Local Work in Federated Learning	Apr 27, 2022	Federated Learning	—Unverified
FedSiam-DA: Dual-aggregated Federated Learning via Siamese Network under Non-IID Data	Nov 17, 2022	Contrastive LearningFederated Learning	—Unverified
FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantification	Apr 24, 2024	Federated LearningPersonalized Federated Learning	—Unverified
Fed-Sim: Federated Simulation for Medical Imaging	Sep 1, 2020	Federated Learning	—Unverified
FedSKD: Aggregation-free Model-heterogeneous Federated Learning using Multi-dimensional Similarity Knowledge Distillation	Mar 23, 2025	Federated LearningKnowledge Distillation	—Unverified
FedSKETCH: Communication-Efficient and Private Federated Learning via Sketching	Aug 11, 2020	Federated Learning	—Unverified
FedSLD: Federated Learning with Shared Label Distribution for Medical Image Classification	Oct 15, 2021	BIG-bench Machine LearningFederated Learning	—Unverified
FedSmart: An Auto Updating Federated Learning Optimization Mechanism	Sep 16, 2020	Federated LearningPrivacy Preserving	—Unverified

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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