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 6201–6225 of 6771 papers

Title	Date	Tasks	Status
FL-Defender: Combating Targeted Attacks in Federated Learning	Jul 2, 2022	Federated Learning	CodeCode Available
Federated Learning via Consensus Mechanism on Heterogeneous Data: A New Perspective on Convergence	Nov 21, 2023	FairnessFederated Learning	CodeCode Available
FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation	Jun 13, 2024	Federated LearningPrivacy Preserving	CodeCode Available
FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation	Dec 4, 2023	Federated LearningPrivacy Preserving	CodeCode Available
Vertical Federated Learning with Missing Features During Training and Inference	Oct 29, 2024	Federated LearningVertical Federated Learning	CodeCode Available
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training	Dec 5, 2017	Federated Learningimage-classification	CodeCode Available
Reliable Federated Disentangling Network for Non-IID Domain Feature	Jan 30, 2023	Federated Learning	CodeCode Available
FLEE-GNN: A Federated Learning System for Edge-Enhanced Graph Neural Network in Analyzing Geospatial Resilience of Multicommodity Food Flows	Oct 20, 2023	Federated LearningGraph Neural Network	CodeCode Available
Towards General Deep Leakage in Federated Learning	Oct 18, 2021	Federated LearningImage Restoration	CodeCode Available
FLeNS: Federated Learning with Enhanced Nesterov-Newton Sketch	Sep 23, 2024	Dimensionality ReductionEdge-computing	CodeCode Available
Personalized Federated Learning via Stacking	Apr 16, 2024	Federated LearningPersonalized Federated Learning	CodeCode Available
When Cars meet Drones: Hyperbolic Federated Learning for Source-Free Domain Adaptation in Adverse Weather	Mar 20, 2024	Autonomous VehiclesDomain Adaptation	CodeCode Available
Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis	Oct 30, 2024	Federated Learning	CodeCode Available
Federated Learning under Partially Class-Disjoint Data via Manifold Reshaping	May 29, 2024	Federated Learning	CodeCode Available
Secure Federated Submodel Learning	Nov 6, 2019	Federated LearningPosition	CodeCode Available
Can we Generalize and Distribute Private Representation Learning?	Oct 5, 2020	Federated LearningGenerative Adversarial Network	CodeCode Available
Deep Gradient Compression Reduce the Communication Bandwidth For distributed Traning	Dec 5, 2017	Federated Learningimage-classification	CodeCode Available
MIA-BAD: An Approach for Enhancing Membership Inference Attack and its Mitigation with Federated Learning	Nov 28, 2023	Federated LearningInference Attack	CodeCode Available
Explainable Federated Bayesian Causal Inference and Its Application in Advanced Manufacturing	Jan 10, 2025	Causal InferenceExplainable artificial intelligence	CodeCode Available
MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updates	Jun 21, 2023	Federated LearningPrivacy Preserving	CodeCode Available
Experimenting with Normalization Layers in Federated Learning on non-IID scenarios	Mar 19, 2023	Federated LearningPrivacy Preserving	CodeCode Available
FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive Models	Mar 5, 2024	Contrastive LearningFederated Learning	CodeCode Available
Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI Models	Oct 1, 2023	DiagnosticDiversity	CodeCode Available
TrojanDam: Detection-Free Backdoor Defense in Federated Learning through Proactive Model Robustification utilizing OOD Data	Apr 22, 2025	backdoor defenseFederated Learning	CodeCode Available
FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality Prediction	May 30, 2022	BIG-bench Machine LearningBinary Classification	CodeCode Available

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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