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 2851–2875 of 6771 papers

Title	Date	Tasks	Status	Hype
Cloud-Magnetic Resonance Imaging System: In the Era of 6G and Artificial Intelligence	Oct 18, 2023	Cloud ComputingDiagnostic	—Unverified	0
Whole-brain radiomics for clustered federated personalization in brain tumor segmentation	Oct 17, 2023	Brain Tumor SegmentationFederated Learning	CodeCode Available	0
Federated Learning with Nonvacuous Generalisation Bounds	Oct 17, 2023	Federated Learning	—Unverified	0
Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learning	Oct 17, 2023	Adversarial RobustnessFederated Learning	—Unverified	0
Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks	Oct 16, 2023	Adversarial AttackFederated Learning	—Unverified	0
Over-the-Air Federated Learning and Optimization	Oct 16, 2023	Federated Learning	—Unverified	0
Passive Inference Attacks on Split Learning via Adversarial Regularization	Oct 16, 2023	Federated Learning	CodeCode Available	1
Federated Learning with Convex Global and Local Constraints	Oct 16, 2023	FairnessFederated Learning	CodeCode Available	0
FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models	Oct 16, 2023	Federated Learningparameter-efficient fine-tuning	CodeCode Available	2
VFLAIR: A Research Library and Benchmark for Vertical Federated Learning	Oct 15, 2023	Federated LearningVertical Federated Learning	CodeCode Available	1
FLrce: Resource-Efficient Federated Learning with Early-Stopping Strategy	Oct 15, 2023	Deep LearningFederated Learning	CodeCode Available	0
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates	Oct 15, 2023	Distributed OptimizationFederated Learning	CodeCode Available	0
Federated Multi-Objective Learning	Oct 15, 2023	Federated LearningMulti-Task Learning	—Unverified	0
Federated Reinforcement Learning for Resource Allocation in V2X Networks	Oct 15, 2023	Federated Learningreinforcement-learning	—Unverified	0
Multimodal Federated Learning in Healthcare: a Review	Oct 14, 2023	Federated Learning	—Unverified	0
A Blockchain-empowered Multi-Aggregator Federated Learning Architecture in Edge Computing with Deep Reinforcement Learning Optimization	Oct 14, 2023	Deep Reinforcement LearningEdge-computing	—Unverified	0
Tackling Heterogeneity in Medical Federated learning via Vision Transformers	Oct 13, 2023	Federated Learning	—Unverified	0
PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning	Oct 13, 2023	Federated Learning	—Unverified	0
Federated Meta-Learning for Few-Shot Fault Diagnosis with Representation Encoding	Oct 13, 2023	DiagnosticFault Diagnosis	—Unverified	0
Price of Stability in Quality-Aware Federated Learning	Oct 13, 2023	DenoisingFederated Learning	—Unverified	0
PAGE: Equilibrate Personalization and Generalization in Federated Learning	Oct 13, 2023	Federated Learning	—Unverified	0
Federated Class-Incremental Learning with Prompting	Oct 13, 2023	class-incremental learningClass Incremental Learning	—Unverified	0
A Carbon Tracking Model for Federated Learning: Impact of Quantization and Sparsification	Oct 12, 2023	Federated LearningQuantization	—Unverified	0
Achieving Linear Speedup with ProxSkip in Distributed Stochastic Optimization	Oct 12, 2023	Distributed OptimizationFederated Learning	—Unverified	0
Sentinel: An Aggregation Function to Secure Decentralized Federated Learning	Oct 12, 2023	Federated LearningManagement	—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