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
Can Fair Federated Learning reduce the need for Personalisation?	May 4, 2023	Federated Learning	—Unverified
A Novel Neural Network-Based Federated Learning System for Imbalanced and Non-IID Data	Nov 16, 2023	Edge-computingFederated Learning	—Unverified
A Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoT	Oct 2, 2024	Edge-computingFederated Learning	—Unverified
A Novel Federated Learning-Based IDS for Enhancing UAVs Privacy and Security	Dec 7, 2023	Federated LearningIntrusion Detection	—Unverified
Advances in Robust Federated Learning: Heterogeneity Considerations	May 16, 2024	DiversityFederated Learning	—Unverified
Accelerated Federated Learning with Decoupled Adaptive Optimization	Jul 14, 2022	Federated Learning	—Unverified
A Novel Attribute Reconstruction Attack in Federated Learning	Aug 16, 2021	AttributeFederated Learning	—Unverified
A Novel Algorithm for Personalized Federated Learning: Knowledge Distillation with Weighted Combination Loss	Apr 6, 2025	Federated LearningKnowledge Distillation	—Unverified
An Optimization Framework for Federated Edge Learning	Nov 26, 2021	Edge-computingFederated Learning	—Unverified
An Optimal Transport Approach to Personalized Federated Learning	Jun 6, 2022	Federated LearningPersonalized Federated Learning	—Unverified
Advances and Open Challenges in Federated Foundation Models	Apr 23, 2024	Computational EfficiencyFederated Learning	—Unverified
A Comparative Study of Sampling Methods with Cross-Validation in the FedHome Framework	Jun 4, 2024	Federated Learning	—Unverified
An Operator Splitting View of Federated Learning	Aug 12, 2021	Federated Learning	—Unverified
Anonymizing Data for Privacy-Preserving Federated Learning	Feb 21, 2020	BIG-bench Machine LearningFederated Learning	—Unverified
Advances and Challenges in Meta-Learning: A Technical Review	Jul 10, 2023	Continual LearningDomain Adaptation	—Unverified
An On-Device Federated Learning Approach for Cooperative Model Update between Edge Devices	Feb 27, 2020	Anomaly DetectionFederated Learning	—Unverified
Advancements of federated learning towards privacy preservation: from federated learning to split learning	Nov 25, 2020	BIG-bench Machine LearningFederated Learning	—Unverified
A Comparative Study of Federated Learning Models for COVID-19 Detection	Mar 28, 2023	Federated LearningMedical Image Analysis	—Unverified
Accelerated Convergence of Stochastic Heavy Ball Method under Anisotropic Gradient Noise	Dec 22, 2023	Federated Learning	—Unverified
Random Client Selection on Contrastive Federated Learning for Tabular Data	May 16, 2025	Federated LearningPrivacy Preserving	—Unverified
Anomaly Detection via Federated Learning	Oct 12, 2022	Anomaly DetectionFederated Learning	—Unverified
Advancements in Federated Learning: Models, Methods, and Privacy	Feb 22, 2023	Federated LearningPrivacy Preserving	—Unverified
Advanced Relay-Based Collaborative Framework for Optimizing Synchronization in Split Federated Learning over Wireless Networks	Mar 18, 2025	Edge-computingFederated Learning	—Unverified
Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach	Jan 22, 2025	Anomaly DetectionDimensionality Reduction	—Unverified
A Comparative Evaluation of FedAvg and Per-FedAvg Algorithms for Dirichlet Distributed Heterogeneous Data	Sep 3, 2023	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