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 3351–3375 of 6771 papers

Title	Date	Tasks	Status
FedMe: Federated Learning via Model Exchange	Oct 15, 2021	BIG-bench Machine LearningFederated Learning	—Unverified
FedMef: Towards Memory-efficient Federated Dynamic Pruning	Mar 21, 2024	Federated LearningNetwork Pruning	—Unverified
Cross-domain Federated Object Detection	Jun 30, 2022	Autonomous DrivingFederated Learning	—Unverified
FedMEKT: Distillation-based Embedding Knowledge Transfer for Multimodal Federated Learning	Jul 25, 2023	Activity RecognitionFederated Learning	—Unverified
FedMeld: A Model-dispersal Federated Learning Framework for Space-ground Integrated Networks	Dec 23, 2024	Federated Learning	—Unverified
FedMentalCare: Towards Privacy-Preserving Fine-Tuned LLMs to Analyze Mental Health Status Using Federated Learning Framework	Feb 27, 2025	Computational EfficiencyFederated Learning	—Unverified
FedMerge: Federated Personalization via Model Merging	Apr 9, 2025	Federated LearningMixture-of-Experts	—Unverified
FedMes: Speeding Up Federated Learning with Multiple Edge Servers	Jan 1, 2021	Federated Learning	—Unverified
FedMetaMed: Federated Meta-Learning for Personalized Medication in Distributed Healthcare Systems	Dec 5, 2024	Federated LearningMeta-Learning	—Unverified
FedMFS: Federated Multimodal Fusion Learning with Selective Modality Communication	Oct 10, 2023	Federated Learning	—Unverified
AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization	Sep 26, 2021	Federated LearningPrivacy Preserving	—Unverified
Federated Learning for Cellular-connected UAVs: Radio Mapping and Path Planning	Aug 23, 2020	Federated Learning	—Unverified
Federated Learning for Breast Density Classification: A Real-World Implementation	Sep 3, 2020	breast density classificationClassification	—Unverified
FedMinds: Privacy-Preserving Personalized Brain Visual Decoding	Sep 3, 2024	Federated LearningPrivacy Preserving	—Unverified
Cross-Domain Federated Learning in Medical Imaging	Dec 18, 2021	Deep LearningFederated Learning	—Unverified
Fed-MIWAE: Federated Imputation of Incomplete Data via Deep Generative Models	Apr 17, 2023	Federated LearningImputation	—Unverified
Federated Learning for Blind Image Super-Resolution	Apr 26, 2024	Federated LearningImage Super-Resolution	—Unverified
Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions	Oct 8, 2021	Federated Learning	—Unverified
A Synergetic Attack against Neural Network Classifiers combining Backdoor and Adversarial Examples	Sep 3, 2021	Federated LearningModel Poisoning	—Unverified
Age-Based Device Selection and Transmit Power Optimization in Over-the-Air Federated Learning	Jan 3, 2025	FairnessFederated Learning	—Unverified
Federated Learning for Autoencoder-based Condition Monitoring in the Industrial Internet of Things	Nov 14, 2022	Federated LearningTransfer Learning	—Unverified
Cross-device Federated Learning for Mobile Health Diagnostics: A First Study on COVID-19 Detection	Mar 13, 2023	DiagnosticFederated Learning	—Unverified
Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks	Feb 7, 2025	Anomaly DetectionFederated Learning	—Unverified
Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights	Dec 7, 2023	Federated Learning	—Unverified
Cross-Cloud Data Privacy Protection: Optimizing Collaborative Mechanisms of AI Systems by Integrating Federated Learning and LLMs	May 19, 2025	Cloud ComputingFederated Learning	—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