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 1976–2000 of 6771 papers

Title	Date	Tasks	Status
DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning Based on Constant-Overhead Linear Secret Resharing	Oct 21, 2024	Federated Learning	—Unverified
Distributed Learning for UAV Swarms	Oct 21, 2024	Federated Learning	—Unverified
Extracting Spatiotemporal Data from Gradients with Large Language Models	Oct 21, 2024	Federated LearningLanguage Modelling	—Unverified
Geographical Node Clustering and Grouping to Guarantee Data IIDness in Federated Learning	Oct 21, 2024	ClusteringFederated Learning	—Unverified
Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification	Oct 21, 2024	Federated LearningFew-Shot Learning	—Unverified
Subword Embedding from Bytes Gains Privacy without Sacrificing Accuracy and Complexity	Oct 21, 2024	Federated LearningLanguage Modeling	—Unverified
A Bayesian Framework for Clustered Federated Learning	Oct 20, 2024	Federated Learning	—Unverified
Tighter Performance Theory of FedExProx	Oct 20, 2024	Distributed OptimizationDiversity	—Unverified
DPVS-Shapley:Faster and Universal Contribution Evaluation Component in Federated Learning	Oct 19, 2024	Contribution AssessmentFederated Learning	—Unverified
Personalized Federated Learning with Adaptive Feature Aggregation and Knowledge Transfer	Oct 19, 2024	Federated LearningPersonalized Federated Learning	—Unverified
A Communication and Computation Efficient Fully First-order Method for Decentralized Bilevel Optimization	Oct 18, 2024	Bilevel OptimizationFederated Learning	—Unverified
Personalizing Low-Rank Bayesian Neural Networks Via Federated Learning	Oct 18, 2024	Decision MakingFederated Learning	—Unverified
FedSpaLLM: Federated Pruning of Large Language Models	Oct 18, 2024	Federated LearningPrivacy Preserving	—Unverified
Comparative Evaluation of Clustered Federated Learning Methods	Oct 18, 2024	Federated Learningimage-classification	CodeCode Available
On-device Federated Learning in Smartphones for Detecting Depression from Reddit Posts	Oct 17, 2024	Depression DetectionFederated Learning	—Unverified
Towards Satellite Non-IID Imagery: A Spectral Clustering-Assisted Federated Learning Approach	Oct 17, 2024	Earth ObservationFederated Learning	—Unverified
Investigating Effective Speaker Property Privacy Protection in Federated Learning for Speech Emotion Recognition	Oct 17, 2024	Emotion RecognitionFederated Learning	—Unverified
FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learning	Oct 17, 2024	Ensemble LearningFederated Learning	—Unverified
Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information	Oct 17, 2024	Conformal PredictionFederated Learning	—Unverified
Federated scientific machine learning for approximating functions and solving differential equations with data heterogeneity	Oct 17, 2024	Federated Learning	—Unverified
Vaccinating Federated Learning for Robust Modulation Classification in Distributed Wireless Networks	Oct 16, 2024	Federated Learning	—Unverified
Disentangling data distribution for Federated Learning	Oct 16, 2024	Federated Learning	—Unverified
DEeR: Deviation Eliminating and Noise Regulating for Privacy-preserving Federated Low-rank Adaptation	Oct 16, 2024	Federated LearningPrivacy Preserving	CodeCode Available
TPFL: A Trustworthy Personalized Federated Learning Framework via Subjective Logic	Oct 16, 2024	Decision MakingFederated Learning	—Unverified
FedGTST: Boosting Global Transferability of Federated Models via Statistics Tuning	Oct 16, 2024	Federated LearningTransfer 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