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 1426–1450 of 6771 papers

Title	Date	Tasks	Status	Hype
DFDG: Data-Free Dual-Generator Adversarial Distillation for One-Shot Federated Learning	Sep 12, 2024	Federated Learningimage-classification	—Unverified	0
Over-the-Air Federated Learning via Weighted Aggregation	Sep 12, 2024	Federated Learning	—Unverified	0
Multi-Model based Federated Learning Against Model Poisoning Attack: A Deep Learning Based Model Selection for MEC Systems	Sep 12, 2024	Deep Reinforcement LearningFederated Learning	—Unverified	0
Privacy-preserving federated prediction of pain intensity change based on multi-center survey data	Sep 12, 2024	Federated LearningPrivacy Preserving	—Unverified	0
FedHide: Federated Learning by Hiding in the Neighbors	Sep 12, 2024	Federated Learning	—Unverified	0
Riemannian Federated Learning via Averaging Gradient Stream	Sep 11, 2024	Federated LearningPrivacy Preserving	—Unverified	0
Federated X-armed Bandit with Flexible Personalisation	Sep 11, 2024	Federated Learning	—Unverified	0
Privacy-Preserving Federated Learning with Consistency via Knowledge Distillation Using Conditional Generator	Sep 11, 2024	DiversityFederated Learning	—Unverified	0
Heterogeneity-Aware Coordination for Federated Learning via Stitching Pre-trained blocks	Sep 11, 2024	Federated Learning	—Unverified	0
Federated Impression for Learning with Distributed Heterogeneous Data	Sep 11, 2024	Federated Learning	CodeCode Available	0
Personalized Federated Learning Techniques: Empirical Analysis	Sep 10, 2024	Federated LearningPersonalized Federated Learning	—Unverified	0
Rate-Constrained Quantization for Communication-Efficient Federated Learning	Sep 10, 2024	Data CompressionFederated Learning	—Unverified	0
Contrastive Federated Learning with Tabular Data Silos	Sep 10, 2024	Contrastive LearningFederated Learning	—Unverified	0
Compute-Update Federated Learning: A Lattice Coding Approach Over-the-Air	Sep 10, 2024	Federated Learning	—Unverified	0
Applied Federated Model Personalisation in the Industrial Domain: A Comparative Study	Sep 10, 2024	Active LearningFederated Learning	—Unverified	0
MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learning	Sep 9, 2024	Federated LearningImage Captioning	—Unverified	0
FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations	Sep 9, 2024	Federated LearningPrivacy Preserving	CodeCode Available	2
pFedGPA: Diffusion-based Generative Parameter Aggregation for Personalized Federated Learning	Sep 9, 2024	DenoisingFederated Learning	—Unverified	0
FedBrain-Distill: Communication-Efficient Federated Brain Tumor Classification Using Ensemble Knowledge Distillation on Non-IID Data	Sep 9, 2024	Brain Tumor ClassificationFederated Learning	CodeCode Available	0
TriplePlay: Enhancing Federated Learning with CLIP for Non-IID Data and Resource Efficiency	Sep 9, 2024	FairnessFederated Learning	—Unverified	0
Balancing Security and Accuracy: A Novel Federated Learning Approach for Cyberattack Detection in Blockchain Networks	Sep 8, 2024	Federated Learning	—Unverified	0
FedFT: Improving Communication Performance for Federated Learning with Frequency Space Transformation	Sep 8, 2024	Federated Learning	CodeCode Available	0
Unified theoretical guarantees for stability, consistency, and convergence in neural PDE solvers from non-IID data to physics-informed networks	Sep 8, 2024	Federated Learning	—Unverified	0
DynamicFL: Federated Learning with Dynamic Communication Resource Allocation	Sep 8, 2024	Federated Learning	CodeCode Available	0
FedModule: A Modular Federated Learning Framework	Sep 7, 2024	Federated LearningPersonalized Federated Learning	CodeCode Available	2

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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