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 826–850 of 6771 papers

Title	Date	Tasks	Status	Score
FedLWS: Federated Learning with Adaptive Layer-wise Weight Shrinking	Mar 19, 2025	Federated Learning	CodeCode Available	5
FedMAP: Unlocking Potential in Personalized Federated Learning through Bi-Level MAP Optimization	May 29, 2024	Federated LearningPersonalized Federated Learning	CodeCode Available	5
Anomaly Localization in Model Gradients Under Backdoor Attacks Against Federated Learning	Nov 29, 2021	Anomaly LocalizationBackdoor Attack	CodeCode Available	5
FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices	Feb 12, 2025	Edge-computingFederated Learning	CodeCode Available	5
FedMRL: Data Heterogeneity Aware Federated Multi-agent Deep Reinforcement Learning for Medical Imaging	Jul 8, 2024	Deep Reinforcement LearningFairness	CodeCode Available	5
FedLion: Faster Adaptive Federated Optimization with Fewer Communication	Feb 15, 2024	Federated Learning	CodeCode Available	5
Anomaly Detection through Unsupervised Federated Learning	Sep 9, 2022	Anomaly DetectionFederated Learning	CodeCode Available	5
FedLF: Adaptive Logit Adjustment and Feature Optimization in Federated Long-Tailed Learning	Sep 18, 2024	Federated Learning	CodeCode Available	5
FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data	Jan 17, 2024	Federated LearningPersonalized Federated Learning	CodeCode Available	5
Adaptive Guidance for Local Training in Heterogeneous Federated Learning	Oct 9, 2024	Federated Learning	CodeCode Available	5
A dual approach for federated learning	Jan 26, 2022	Federated Learning	CodeCode Available	5
FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation	Sep 30, 2023	Federated Learning	CodeCode Available	5
An Interpretable Client Decision Tree Aggregation process for Federated Learning	Apr 3, 2024	Federated Learning	CodeCode Available	5
An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models	Feb 11, 2025	Federated LearningPrivacy Preserving	CodeCode Available	5
A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks	May 10, 2022	Federated Learning	CodeCode Available	5
FedKD-hybrid: Federated Hybrid Knowledge Distillation for Lithography Hotspot Detection	Jan 7, 2025	Federated LearningKnowledge Distillation	CodeCode Available	5
FedICT: Federated Multi-task Distillation for Multi-access Edge Computing	Jan 1, 2023	Edge-computingFederated Learning	CodeCode Available	5
FedPH: Privacy-enhanced Heterogeneous Federated Learning	Jan 27, 2023	Federated LearningPrivacy Preserving	CodeCode Available	5
FedIA: Federated Medical Image Segmentation with Heterogeneous Annotation Completeness	Jul 2, 2024	Federated LearningImage Segmentation	CodeCode Available	5
FedIMPUTE: Privacy-Preserving Missing Value Imputation for Multi-Site Heterogeneous Electronic Health Records	Aug 22, 2024	Federated LearningMissing Values	CodeCode Available	5
FEDKIM: Adaptive Federated Knowledge Injection into Medical Foundation Models	Aug 17, 2024	Federated LearningMixture-of-Experts	CodeCode Available	5
FedHarmony: Unlearning Scanner Bias with Distributed Data	May 31, 2022	Federated Learning	CodeCode Available	5
A Distributed Generative AI Approach for Heterogeneous Multi-Domain Environments under Data Sharing constraints	Jul 17, 2025	Federated LearningImage Generation	CodeCode Available	5
An Improved Algorithm for Clustered Federated Learning	Oct 20, 2022	ClusteringFederated Learning	CodeCode Available	5
FedGS: Federated Gradient Scaling for Heterogeneous Medical Image Segmentation	Aug 21, 2024	DisentanglementFederated Learning	CodeCode Available	5

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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