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 5451–5475 of 6771 papers

Title	Date	Tasks	Status
A review of federated learning in renewable energy applications: Potential, challenges, and future directions	Dec 18, 2023	Federated LearningPrivacy Preserving	—Unverified
A Review of Privacy-preserving Federated Learning for the Internet-of-Things	Apr 24, 2020	BIG-bench Machine LearningFederated Learning	—Unverified
A review on different techniques used to combat the non-IID and heterogeneous nature of data in FL	Jan 1, 2024	Federated Learning	—Unverified
Identifying Backdoor Attacks in Federated Learning via Anomaly Detection	Feb 9, 2022	Anomaly DetectionFederated Learning	—Unverified
A Robust Federated Learning Framework for Undependable Devices at Scale	Dec 28, 2024	Federated Learning	—Unverified
Artificial Intelligence-Driven Clinical Decision Support Systems	Jan 16, 2025	FairnessFederated Learning	—Unverified
Artificial Intelligence Driven UAV-NOMA-MEC in Next Generation Wireless Networks	Jan 27, 2021	Edge-computingFederated Learning	—Unverified
Artificial Intelligence for Personalized Prediction of Alzheimer's Disease Progression: A Survey of Methods, Data Challenges, and Future Directions	Apr 29, 2025	Causal InferenceFederated Learning	—Unverified
Artificial Intelligence in Pediatric Echocardiography: Exploring Challenges, Opportunities, and Clinical Applications with Explainable AI and Federated Learning	Nov 15, 2024	Decision MakingDiagnostic	—Unverified
A Safe Deep Reinforcement Learning Approach for Energy Efficient Federated Learning in Wireless Communication Networks	Aug 21, 2023	Deep Reinforcement LearningFederated Learning	—Unverified
A Safe Genetic Algorithm Approach for Energy Efficient Federated Learning in Wireless Communication Networks	Jun 25, 2023	Federated Learning	—Unverified
A Secure Aggregation for Federated Learning on Long-Tailed Data	Jul 17, 2023	Federated Learning	—Unverified
A Secure and Efficient Federated Learning Framework for NLP	Jan 28, 2022	Federated Learning	—Unverified
A Secure and Trustworthy Network Architecture for Federated Learning Healthcare Applications	Apr 17, 2024	Federated LearningPrivacy Preserving	—Unverified
A Secure Federated Learning Framework for Residential Short Term Load Forecasting	Sep 29, 2022	Demand ForecastingFederated Learning	—Unverified
A Secure Federated Learning Framework for 5G Networks	May 12, 2020	Federated Learning	—Unverified
A Secure Healthcare 5.0 System Based on Blockchain Technology Entangled with Federated Learning Technique	Sep 16, 2022	Federated LearningIntrusion Detection	—Unverified
A Selective Homomorphic Encryption Approach for Faster Privacy-Preserving Federated Learning	Jan 22, 2025	Computational EfficiencyDiagnostic	—Unverified
A SER-based Device Selection Mechanism in Multi-bits Quantization Federated Learning	Apr 20, 2024	Federated LearningQuantization	—Unverified
A Simple Data Augmentation for Feature Distribution Skewed Federated Learning	Jun 14, 2023	Data AugmentationFederated Learning	—Unverified
A Snapshot of the Frontiers of Client Selection in Federated Learning	Sep 27, 2022	Federated LearningPrivacy Preserving	—Unverified
Assortment of Attention Heads: Accelerating Federated PEFT with Head Pruning and Strategic Client Selection	May 31, 2025	Federated Learningparameter-efficient fine-tuning	—Unverified
A State Alignment-Centric Approach to Federated System Identification: The FedAlign Framework	Mar 15, 2025	Federated LearningState Space Models	—Unverified
A Stochastic Gradient Langevin Dynamics Algorithm For Noise Intrinsic Federated Learning	Jan 1, 2021	Federated Learning	—Unverified
A Study of Secure Algorithms for Vertical Federated Learning: Take Secure Logistic Regression as an Example	Oct 30, 2024	Federated LearningVertical Federated 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