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 1301–1325 of 6771 papers

Title	Date	Tasks	Status
KnowledgeSG: Privacy-Preserving Synthetic Text Generation with Knowledge Distillation from Server	Oct 8, 2024	Federated LearningKnowledge Distillation	CodeCode Available
FairFML: Fair Federated Machine Learning with a Case Study on Reducing Gender Disparities in Cardiac Arrest Outcome Prediction	Oct 7, 2024	FairnessFederated Learning	CodeCode Available
Federated brain tumor segmentation: an extensive benchmark	Oct 7, 2024	Brain Tumor SegmentationFederated Learning	—Unverified
Over-the-Air Federated Learning in Cell-Free MIMO with Long-term Power Constraint	Oct 7, 2024	Distributed ComputingFederated Learning	—Unverified
Aiding Global Convergence in Federated Learning via Local Perturbation and Mutual Similarity Information	Oct 7, 2024	Distributed OptimizationFederated Learning	—Unverified
Federated Learning Nodes Can Reconstruct Peers' Image Data	Oct 7, 2024	Federated LearningPrivacy Preserving	—Unverified
FRIDA: Free-Rider Detection using Privacy Attacks	Oct 7, 2024	Federated Learning	—Unverified
FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models	Oct 7, 2024	Federated Learning	—Unverified
pFedGame -- Decentralized Federated Learning using Game Theory in Dynamic Topology	Oct 5, 2024	Federated LearningModel Poisoning	—Unverified
ConDa: Fast Federated Unlearning with Contribution Dampening	Oct 5, 2024	Federated Learning	—Unverified
Deep Domain Isolation and Sample Clustered Federated Learning for Semantic Segmentation	Oct 4, 2024	Federated LearningSemantic Segmentation	CodeCode Available
An Intelligent Native Network Slicing Security Architecture Empowered by Federated Learning	Oct 4, 2024	Federated Learning	—Unverified
BN-SCAFFOLD: controlling the drift of Batch Normalization statistics in Federated Learning	Oct 4, 2024	Federated Learning	—Unverified
FedMAC: Tackling Partial-Modality Missing in Federated Learning with Cross-Modal Aggregation and Contrastive Regularization	Oct 4, 2024	Federated Learning	—Unverified
A Survey on Group Fairness in Federated Learning: Challenges, Taxonomy of Solutions and Directions for Future Research	Oct 4, 2024	FairnessFederated Learning	CodeCode Available
Collaborative and Efficient Personalization with Mixtures of Adaptors	Oct 4, 2024	Federated LearningMulti-Task Learning	—Unverified
Influence-oriented Personalized Federated Learning	Oct 4, 2024	Federated LearningPersonalized Federated Learning	—Unverified
FedCert: Federated Accuracy Certification	Oct 4, 2024	Federated Learning	CodeCode Available
FedStein: Enhancing Multi-Domain Federated Learning Through James-Stein Estimator	Oct 4, 2024	Domain AdaptationDomain Generalization	CodeCode Available
Accelerating Deep Learning with Fixed Time Budget	Oct 3, 2024	Deep LearningFederated Learning	—Unverified
FedPeWS: Personalized Warmup via Subnetworks for Enhanced Heterogeneous Federated Learning	Oct 3, 2024	Federated Learning	—Unverified
Personalized Federated Learning for Generative AI-Assisted Semantic Communications	Oct 3, 2024	Federated LearningNetwork Pruning	—Unverified
Towards Layer-Wise Personalized Federated Learning: Adaptive Layer Disentanglement via Conflicting Gradients	Oct 3, 2024	DisentanglementFederated Learning	—Unverified
FedScalar: A Communication efficient Federated Learning	Oct 3, 2024	Federated Learning	—Unverified
A Survey on Point-of-Interest Recommendation: Models, Architectures, and Security	Oct 3, 2024	Decision MakingFederated 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