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 2976–3000 of 6771 papers

Title	Date	Tasks	Status
Federated Learning with Projected Trajectory Regularization	Dec 22, 2023	Federated Learning	—Unverified
Federated Learning With Quantized Global Model Updates	Jun 18, 2020	Federated Learningmodel	—Unverified
Federated PCA and Estimation for Spiked Covariance Matrices: Optimal Rates and Efficient Algorithm	Nov 23, 2024	Federated Learning	—Unverified
FedCC: Robust Federated Learning against Model Poisoning Attacks	Dec 5, 2022	Federated Learningmodel	—Unverified
Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency	Nov 9, 2023	Deep LearningFederated Learning	—Unverified
Federated Learning with Regularized Client Participation	Feb 7, 2023	Federated Learning	—Unverified
Federated Learning with Relative Fairness	Nov 2, 2024	Computational EfficiencyFairness	—Unverified
Federated Learning with Research Prototypes for Multi-Center MRI-based Detection of Prostate Cancer with Diverse Histopathology	Jun 11, 2022	Deep LearningFederated Learning	—Unverified
Deep Learning Model Security: Threats and Defenses	Dec 12, 2024	Data PoisoningDeep Learning	—Unverified
Federated Learning with Sample-level Client Drift Mitigation	Jan 20, 2025	Federated Learning	—Unverified
Federated Learning with Server Learning: Enhancing Performance for Non-IID Data	Oct 6, 2022	Auxiliary LearningFederated Learning	—Unverified
A Federated Channel Modeling System using Generative Neural Networks	May 30, 2023	Federated LearningGenerative Adversarial Network	—Unverified
Federated prediction for scalable and privacy-preserved knowledge-based planning in radiotherapy	May 20, 2025	Federated Learning	—Unverified
Communication-Efficient Decentralized Learning with Sparsification and Adaptive Peer Selection	Feb 22, 2020	Federated Learning	—Unverified
Federated Learning with Unbiased Gradient Aggregation and Controllable Meta Updating	Oct 18, 2019	Federated Learning	—Unverified
FedCCL: Federated Dual-Clustered Feature Contrast Under Domain Heterogeneity	Apr 14, 2024	ClusteringFederated Learning	—Unverified
Deep Quality Estimation: Creating Surrogate Models for Human Quality Ratings	May 17, 2022	Federated LearningSegmentation	—Unverified
Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions	May 21, 2025	Federated LearningPersonalized Federated Learning	—Unverified
Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT	Feb 3, 2022	Deep Reinforcement LearningFederated Learning	—Unverified
Federated Learning with Workload Reduction through Partial Training of Client Models and Entropy-Based Data Selection	Dec 30, 2024	Federated LearningPrivacy Preserving	—Unverified
Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-term Utility Demand Forecasting in Electricity Wholesale Markets	Jun 23, 2022	Deep Reinforcement LearningDemand Forecasting	—Unverified
Federated Linear Contextual Bandits with Heterogeneous Clients	Feb 29, 2024	AllFederated Learning	—Unverified
Deep Reinforcement Learning Based Vehicle Selection for Asynchronous Federated Learning Enabled Vehicular Edge Computing	Apr 6, 2023	Deep Reinforcement LearningEdge-computing	—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
FedCCEA : A Practical Approach of Client Contribution Evaluation for Federated Learning	Jun 4, 2021	Data ValuationFederated 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