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 2201–2225 of 6771 papers

Title	Date	Tasks	Status
1-Bit Compressive Sensing for Efficient Federated Learning Over the Air	Mar 30, 2021	Compressive SensingDimensionality Reduction	—Unverified
FedCPC: An Effective Federated Contrastive Learning Method for Privacy Preserving Early-Stage Alzheimer's Speech Detection	Nov 21, 2023	Contrastive LearningFederated Learning	—Unverified
Contextual Model Aggregation for Fast and Robust Federated Learning in Edge Computing	Mar 23, 2022	Edge-computingFederated Learning	—Unverified
Contextual Combinatorial Multi-output GP Bandits with Group Constraints	Nov 29, 2021	Federated LearningGaussian Processes	—Unverified
A Survey on Vertical Federated Learning: From a Layered Perspective	Apr 4, 2023	Federated LearningPrivacy Preserving	—Unverified
Context-Aware Online Client Selection for Hierarchical Federated Learning	Dec 2, 2021	Federated Learning	—Unverified
Content Popularity Prediction in Fog-RANs: A Clustered Federated Learning Based Approach	Jun 13, 2022	Federated Learning	—Unverified
A Survey on the Role of Artificial Intelligence and Machine Learning in 6G-V2X Applications	Jun 11, 2025	Autonomous VehiclesFederated Learning	—Unverified
Affect-driven Ordinal Engagement Measurement from Video	Jun 21, 2021	Federated LearningOrdinal Classification	—Unverified
Content Popularity Prediction Based on Quantized Federated Bayesian Learning in Fog Radio Access Networks	Jun 23, 2022	Federated Learning	—Unverified
A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computing	Mar 24, 2023	Distributed ComputingFederated Learning	—Unverified
AdapterFL: Adaptive Heterogeneous Federated Learning for Resource-constrained Mobile Computing Systems	Nov 23, 2023	Federated Learning	—Unverified
A Survey on Point-of-Interest Recommendation: Models, Architectures, and Security	Oct 3, 2024	Decision MakingFederated Learning	—Unverified
Constrained Differentially Private Federated Learning for Low-bandwidth Devices	Feb 27, 2021	Federated LearningPrivacy Preserving	—Unverified
A few-shot Label Unlearning in Vertical Federated Learning	Oct 14, 2024	Computational EfficiencyFederated Learning	—Unverified
FedControl: When Control Theory Meets Federated Learning	May 27, 2022	Federated Learning	—Unverified
Considerations on the Theory of Training Models with Differential Privacy	Mar 8, 2023	Federated Learning	—Unverified
Connecting Federated ADMM to Bayes	Jan 28, 2025	Federated Learning	—Unverified
A Survey on Participant Selection for Federated Learning in Mobile Networks	Jul 8, 2022	Federated LearningPrivacy Preserving	—Unverified
A Survey on Model-based, Heuristic, and Machine Learning Optimization Approaches in RIS-aided Wireless Networks	Mar 25, 2023	Federated LearningGraph Learning	—Unverified
Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information	Oct 17, 2024	Conformal PredictionFederated Learning	—Unverified
A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs	Sep 27, 2024	AllFederated Learning	—Unverified
Conformal Prediction for Federated Uncertainty Quantification Under Label Shift	Jun 8, 2023	Conformal PredictionFederated Learning	—Unverified
Conformalized Prediction of Post-Fault Voltage Trajectories Using Pre-trained and Finetuned Attention-Driven Neural Operators	Oct 31, 2024	Conformal PredictionFederated Learning	—Unverified
A Survey on Heterogeneous Federated Learning	Oct 10, 2022	Federated LearningSurvey	—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