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 1051–1075 of 6771 papers

Title	Date	Tasks	Status	Hype
FedQP: Towards Accurate Federated Learning using Quadratic Programming Guided Mutation	Nov 24, 2024	Federated LearningPrivacy Preserving	—Unverified	0
Modality Alignment Meets Federated Broadcasting	Nov 24, 2024	Federated Learning	—Unverified	0
Tackling Data Heterogeneity in Federated Time Series Forecasting	Nov 24, 2024	Federated LearningPrivacy Preserving	—Unverified	0
eFedLLM: Efficient LLM Inference Based on Federated Learning	Nov 24, 2024	Federated Learning	—Unverified	0
Federated Learning in Chemical Engineering: A Tutorial on a Framework for Privacy-Preserving Collaboration Across Distributed Data Sources	Nov 23, 2024	Data IntegrationDrug Discovery	CodeCode Available	0
Federated PCA and Estimation for Spiked Covariance Matrices: Optimal Rates and Efficient Algorithm	Nov 23, 2024	Federated Learning	—Unverified	0
Partial Knowledge Distillation for Alleviating the Inherent Inter-Class Discrepancy in Federated Learning	Nov 23, 2024	Federated LearningKnowledge Distillation	—Unverified	0
FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data	Nov 22, 2024	Federated Learning	CodeCode Available	1
Geminio: Language-Guided Gradient Inversion Attacks in Federated Learning	Nov 22, 2024	Federated Learning	CodeCode Available	0
LoRA-FAIR: Federated LoRA Fine-Tuning with Aggregation and Initialization Refinement	Nov 22, 2024	Federated Learningparameter-efficient fine-tuning	—Unverified	0
Memory Backdoor Attacks on Neural Networks	Nov 21, 2024	Backdoor AttackFederated Learning	—Unverified	0
FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous Vehicles	Nov 21, 2024	Autonomous DrivingAutonomous Vehicles	CodeCode Available	0
REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting	Nov 21, 2024	Federated Learning	CodeCode Available	0
Split Federated Learning Over Heterogeneous Edge Devices: Algorithm and Optimization	Nov 21, 2024	Federated Learning	—Unverified	0
On-device Content-based Recommendation with Single-shot Embedding Pruning: A Cooperative Game Perspective	Nov 20, 2024	Federated LearningRecommendation Systems	CodeCode Available	0
NCAirFL: CSI-Free Over-the-Air Federated Learning Based on Non-Coherent Detection	Nov 20, 2024	Federated Learning	—Unverified	0
Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions	Nov 19, 2024	Federated LearningPrivacy Preserving	—Unverified	0
Hyper-parameter Optimization for Federated Learning with Step-wise Adaptive Mechanism	Nov 19, 2024	AutoMLFederated Learning	—Unverified	0
Attribute Inference Attacks for Federated Regression Tasks	Nov 19, 2024	AttributeFederated Learning	CodeCode Available	0
DeTrigger: A Gradient-Centric Approach to Backdoor Attack Mitigation in Federated Learning	Nov 19, 2024	Adversarial AttackBackdoor Attack	—Unverified	0
Federated Contrastive Learning of Graph-Level Representations	Nov 18, 2024	ClusteringContrastive Learning	—Unverified	0
FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for Federated Learning	Nov 18, 2024	Federated LearningPrivacy Preserving	—Unverified	0
Freezing of Gait Detection Using Gramian Angular Fields and Federated Learning from Wearable Sensors	Nov 18, 2024	Federated LearningMissing Values	CodeCode Available	0
Towards Federated Graph Learning in One-shot Communication	Nov 18, 2024	Federated LearningGraph Learning	—Unverified	0
A Potential Game Perspective in Federated Learning	Nov 18, 2024	Federated Learning	CodeCode Available	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