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 2401–2425 of 6771 papers

Title	Date	Tasks	Status
FedEM: A Privacy-Preserving Framework for Concurrent Utility Preservation in Federated Learning	Mar 8, 2025	Federated LearningPrivacy Preserving	—Unverified
FedEmb: A Vertical and Hybrid Federated Learning Algorithm using Network And Feature Embedding Aggregation	Nov 30, 2023	Federated LearningPrivacy Preserving	—Unverified
FedEmbed: Personalized Private Federated Learning	Feb 18, 2022	Federated Learning	—Unverified
Communication-Efficient Wireless Federated Fine-Tuning for Large-Scale AI Models	May 1, 2025	Federated Learning	—Unverified
A Survey for Large Language Models in Biomedicine	Aug 29, 2024	DiagnosticDrug Discovery	—Unverified
FedEPA: Enhancing Personalization and Modality Alignment in Multimodal Federated Learning	Apr 16, 2025	Contrastive LearningDiversity	—Unverified
FedDiscrete: A Secure Federated Learning Algorithm Against Weight Poisoning	Sep 29, 2021	Federated Learning	—Unverified
FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition	Apr 29, 2024	Federated Learningparameter-efficient fine-tuning	—Unverified
Federal Learning Framework for Quality Evaluation of Blastomere Cleavage	Dec 21, 2024	Federated Learning	—Unverified
FedeRank: User Controlled Feedback with Federated Recommender Systems	Dec 15, 2020	DiversityFederated Learning	—Unverified
Federated Action Recognition on Heterogeneous Embedded Devices	Jul 18, 2021	Action RecognitionFederated Learning	—Unverified
Federated Active Learning (F-AL): an Efficient Annotation Strategy for Federated Learning	Feb 1, 2022	Active LearningDistributed Optimization	—Unverified
Communication-robust and Privacy-safe Distributed Estimation for Heterogeneous Community-level Behind-the-meter Solar Power Generation	Aug 15, 2024	Federated LearningScheduling	—Unverified
Federated Active Learning Framework for Efficient Annotation Strategy in Skin-lesion Classification	Jun 17, 2024	Active LearningFederated Learning	—Unverified
A Simple Data Augmentation for Feature Distribution Skewed Federated Learning	Jun 14, 2023	Data AugmentationFederated Learning	—Unverified
Federated Adaptation of Reservoirs via Intrinsic Plasticity	May 25, 2022	Federated Learning	—Unverified
Federated Adapter on Foundation Models: An Out-Of-Distribution Approach	May 2, 2025	Federated Learningparameter-efficient fine-tuning	—Unverified
Federated Discrete Denoising Diffusion Model for Molecular Generation with OpenFL	Jan 21, 2025	DenoisingDrug Design	—Unverified
FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination	Apr 7, 2023	Federated LearningPrivacy Preserving	—Unverified
Federated Adversarial Learning: A Framework with Convergence Analysis	Aug 7, 2022	Federated Learning	—Unverified
Federated Adversarial Learning for Robust Autonomous Landing Runway Detection	Jun 22, 2024	Federated LearningLane Detection	—Unverified
Federated Adversarial Training with Transformers	Jun 5, 2022	Federated Learning	—Unverified
Federated AI lets a team imagine together: Federated Learning of GANs	Jun 9, 2019	Federated Learning	—Unverified
Federated Alternate Training (FAT): Leveraging Unannotated Data Silos in Federated Segmentation for Medical Imaging	Apr 18, 2023	Federated LearningSegmentation	—Unverified
A SER-based Device Selection Mechanism in Multi-bits Quantization Federated Learning	Apr 20, 2024	Federated LearningQuantization	—Unverified

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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