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 601–625 of 6771 papers

Title	Date	Tasks	Status	Hype
Federated Learning Meets Fluid Antenna: Towards Robust and Scalable Edge Intelligence	Mar 4, 2025	DiversityFederated Learning	—Unverified	0
Federated nnU-Net for Privacy-Preserving Medical Image Segmentation	Mar 4, 2025	Federated LearningImage Segmentation	CodeCode Available	1
AugFL: Augmenting Federated Learning with Pretrained Models	Mar 4, 2025	Federated LearningMeta-Learning	CodeCode Available	0
Federated Learning for Privacy-Preserving Feedforward Control in Multi-Agent Systems	Mar 4, 2025	Autonomous DrivingFederated Learning	CodeCode Available	0
Leveraging Randomness in Model and Data Partitioning for Privacy Amplification	Mar 4, 2025	Federated Learning	—Unverified	0
Federated Learning Framework via Distributed Mutual Learning	Mar 3, 2025	Federated Learning	—Unverified	0
MAB-Based Channel Scheduling for Asynchronous Federated Learning in Non-Stationary Environments	Mar 3, 2025	FairnessFederated Learning	—Unverified	0
A Lightweight and Secure Deep Learning Model for Privacy-Preserving Federated Learning in Intelligent Enterprises	Mar 3, 2025	Federated LearningPrivacy Preserving	CodeCode Available	0
GRAIN: Exact Graph Reconstruction from Gradients	Mar 3, 2025	Federated LearningGraph Attention	CodeCode Available	0
Heterogeneity Matters even More in Distributed Learning: Study from Generalization Perspective	Mar 3, 2025	Federated Learning	—Unverified	0
Patient-Level Anatomy Meets Scanning-Level Physics: Personalized Federated Low-Dose CT Denoising Empowered by Large Language Model	Mar 2, 2025	AnatomyDenoising	CodeCode Available	0
Unmasking Digital Falsehoods: A Comparative Analysis of LLM-Based Misinformation Detection Strategies	Mar 2, 2025	Fact CheckingFederated Learning	—Unverified	0
Asynchronous Personalized Federated Learning through Global Memorization	Mar 1, 2025	Federated LearningMemorization	—Unverified	0
Communication-Efficient Device Scheduling for Federated Learning Using Lyapunov Optimization	Mar 1, 2025	Federated LearningScheduling	—Unverified	0
Conditioning on Local Statistics for Scalable Heterogeneous Federated Learning	Mar 1, 2025	Federated Learning	—Unverified	0
FLStore: Efficient Federated Learning Storage for non-training workloads	Mar 1, 2025	Federated LearningPrivacy Preserving	CodeCode Available	0
FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated Clients	Feb 28, 2025	Federated Learning	CodeCode Available	1
QFAL: Quantum Federated Adversarial Learning	Feb 28, 2025	Adversarial AttackFederated Learning	—Unverified	0
Fed-KAN: Federated Learning with Kolmogorov-Arnold Networks for Traffic Prediction	Feb 28, 2025	Federated LearningKolmogorov-Arnold Networks	—Unverified	0
FedDyMem: Efficient Federated Learning with Dynamic Memory and Memory-Reduce for Unsupervised Image Anomaly Detection	Feb 28, 2025	Anomaly DetectionFederated Learning	—Unverified	0
FedMentalCare: Towards Privacy-Preserving Fine-Tuned LLMs to Analyze Mental Health Status Using Federated Learning Framework	Feb 27, 2025	Computational EfficiencyFederated Learning	—Unverified	0
DPZV: Elevating the Tradeoff between Privacy and Utility in Zeroth-Order Vertical Federated Learning	Feb 27, 2025	Federated LearningVertical Federated Learning	—Unverified	0
Revisit the Stability of Vanilla Federated Learning Under Diverse Conditions	Feb 27, 2025	ClassificationFederated Learning	—Unverified	0
Can Textual Gradient Work in Federated Learning?	Feb 27, 2025	Federated Learning	CodeCode Available	1
Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data	Feb 27, 2025	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