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 2126–2150 of 6771 papers

Title	Date	Tasks	Status	Hype
SoK: On Gradient Leakage in Federated Learning	Apr 8, 2024	Federated LearningMisconceptions	—Unverified	0
PlateSegFL: A Privacy-Preserving License Plate Detection Using Federated Segmentation Learning	Apr 7, 2024	Federated LearningImage Segmentation	—Unverified	0
Hyperparameter Optimization for SecureBoost via Constrained Multi-Objective Federated Learning	Apr 6, 2024	Bayesian OptimizationFederated Learning	—Unverified	0
Vanishing Variance Problem in Fully Decentralized Neural-Network Systems	Apr 6, 2024	Federated Learning	—Unverified	0
Prompt Public Large Language Models to Synthesize Data for Private On-device Applications	Apr 5, 2024	Federated Learning	—Unverified	0
Distributionally Robust Alignment for Medical Federated Vision-Language Pre-training Under Data Heterogeneity	Apr 5, 2024	cross-modal alignmentFederated Learning	—Unverified	0
Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning	Apr 5, 2024	Data PoisoningFederated Learning	—Unverified	0
Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach	Apr 4, 2024	Contrastive LearningFederated Learning	—Unverified	0
Approximate Gradient Coding for Privacy-Flexible Federated Learning with Non-IID Data	Apr 4, 2024	Federated Learning	CodeCode Available	0
Exploring Lightweight Federated Learning for Distributed Load Forecasting	Apr 4, 2024	Federated LearningLoad Forecasting	—Unverified	0
An Interpretable Client Decision Tree Aggregation process for Federated Learning	Apr 3, 2024	Federated Learning	CodeCode Available	0
FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning	Apr 3, 2024	Federated LearningPersonalized Federated Learning	CodeCode Available	1
Robust Federated Learning for Wireless Networks: A Demonstration with Channel Estimation	Apr 3, 2024	Federated LearningPrivacy Preserving	—Unverified	0
Federated Computing -- Survey on Building Blocks, Extensions and Systems	Apr 3, 2024	Federated LearningSurvey	—Unverified	0
QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detection	Apr 3, 2024	Federated LearningFraud Detection	—Unverified	0
Optimal Batch Allocation for Wireless Federated Learning	Apr 3, 2024	Federated Learning	—Unverified	0
Federated Distillation: A Survey	Apr 2, 2024	Federated LearningKnowledge Distillation	—Unverified	0
Federated Multi-Agent Mapping for Planetary Exploration	Apr 2, 2024	Federated Learning	—Unverified	0
Privacy-Aware Spectrum Pricing and Power Control Optimization for LEO Satellite Internet-of-Things	Apr 1, 2024	Deep Reinforcement LearningFederated Learning	—Unverified	0
Addressing Heterogeneity in Federated Load Forecasting with Personalization Layers	Apr 1, 2024	Federated LearningLoad Forecasting	—Unverified	0
Open-Vocabulary Federated Learning with Multimodal Prototyping	Apr 1, 2024	Federated Learning	CodeCode Available	0
Continual Learning for Smart City: A Survey	Apr 1, 2024	Continual LearningFederated Learning	—Unverified	0
Supplementary File: Cooperative Gradient Coding for Semi-Decentralized Federated Learning	Mar 31, 2024	Federated Learningvalid	—Unverified	0
Computation and Communication Efficient Lightweighting Vertical Federated Learning for Smart Building IoT	Mar 30, 2024	Computational EfficiencyFederated Learning	—Unverified	0
From Learning to Analytics: Improving Model Efficacy with Goal-Directed Client Selection	Mar 30, 2024	Federated Learning	—Unverified	0

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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