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 3451–3475 of 6771 papers

Title	Date	Tasks	Status
Data-Free Distillation Improves Efficiency and Privacy in Federated Thorax Disease Analysis	Oct 22, 2023	Federated LearningPrivacy Preserving	—Unverified
The Hidden Adversarial Vulnerabilities of Medical Federated Learning	Oct 21, 2023	Federated LearningMedical Image Analysis	—Unverified
Goal-oriented Communications for the IoT: System Design and Adaptive Resource Optimization	Oct 21, 2023	Federated Learning	—Unverified
Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devices	Oct 21, 2023	Data AugmentationFederated Learning	—Unverified
Competitive Advantage Attacks to Decentralized Federated Learning	Oct 20, 2023	Federated Learning	—Unverified
pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning	Oct 20, 2023	Federated LearningPersonalized Federated Learning	—Unverified
VFedMH: Vertical Federated Learning for Training Multiple Heterogeneous Models	Oct 20, 2023	Federated LearningVertical Federated Learning	—Unverified
An Efficient Federated Learning Framework for Training Semantic Communication System	Oct 20, 2023	Federated LearningSemantic Communication	—Unverified
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
Can We Trust the Similarity Measurement in Federated Learning?	Oct 20, 2023	Federated LearningModel Poisoning	—Unverified
BRFL: A Blockchain-based Byzantine-Robust Federated Learning Model	Oct 20, 2023	Federated Learning	—Unverified
DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Dataset	Oct 20, 2023	Federated LearningNavigate	—Unverified
A Distributed Approach to Meteorological Predictions: Addressing Data Imbalance in Precipitation Prediction Models through Federated Learning and GANs	Oct 19, 2023	Data AugmentationFederated Learning	—Unverified
Fact-based Agent modeling for Multi-Agent Reinforcement Learning	Oct 18, 2023	Federated LearningMulti-agent Reinforcement Learning	—Unverified
Effective and Efficient Federated Tree Learning on Hybrid Data	Oct 18, 2023	Federated Learning	—Unverified
Equipping Federated Graph Neural Networks with Structure-aware Group Fairness	Oct 18, 2023	FairnessFederated Learning	CodeCode Available
On the Distributed Evaluation of Generative Models	Oct 18, 2023	AvgFederated Learning	—Unverified
Cloud-Magnetic Resonance Imaging System: In the Era of 6G and Artificial Intelligence	Oct 18, 2023	Cloud ComputingDiagnostic	—Unverified
Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learning	Oct 17, 2023	Adversarial RobustnessFederated Learning	—Unverified
Federated Learning with Nonvacuous Generalisation Bounds	Oct 17, 2023	Federated Learning	—Unverified
Whole-brain radiomics for clustered federated personalization in brain tumor segmentation	Oct 17, 2023	Brain Tumor SegmentationFederated Learning	CodeCode Available
Over-the-Air Federated Learning and Optimization	Oct 16, 2023	Federated Learning	—Unverified
Federated Learning with Convex Global and Local Constraints	Oct 16, 2023	FairnessFederated Learning	CodeCode Available
Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks	Oct 16, 2023	Adversarial AttackFederated Learning	—Unverified
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates	Oct 15, 2023	Distributed OptimizationFederated Learning	CodeCode Available

Show:10 25 50

← PrevPage 139 of 271Next →

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