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 4476–4500 of 6771 papers

Title	Date	Tasks	Status
FedVMR: A New Federated Learning method for Video Moment Retrieval	Oct 28, 2022	Federated LearningMoment Retrieval	—Unverified
FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data	Mar 5, 2021	Federated LearningPrivacy Preserving	—Unverified
FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models	Apr 26, 2023	Federated LearningPrivacy Preserving	—Unverified
Fedward: Flexible Federated Backdoor Defense Framework with Non-IID Data	Jul 1, 2023	backdoor defenseClustering	—Unverified
FedWOA: A Federated Learning Model that uses the Whale Optimization Algorithm for Renewable Energy Prediction	Sep 19, 2023	Federated LearningPrediction	—Unverified
FedWSIDD: Federated Whole Slide Image Classification via Dataset Distillation	Jun 18, 2025	ClassificationDataset Distillation	—Unverified
FedX: Adaptive Model Decomposition and Quantization for IoT Federated Learning	Apr 17, 2025	Federated LearningQuantization	—Unverified
FedXGBoost: Privacy-Preserving XGBoost for Federated Learning	Jun 20, 2021	Federated LearningPrivacy Preserving	—Unverified
FedYolo: Augmenting Federated Learning with Pretrained Transformers	Jul 10, 2023	Federated Learning	—Unverified
FedZeN: Towards superlinear zeroth-order federated learning via incremental Hessian estimation	Sep 29, 2023	Federated LearningPrivacy Preserving	—Unverified
Learning to Specialize: Joint Gating-Expert Training for Adaptive MoEs in Decentralized Settings	Jun 14, 2023	DiversityFederated Learning	—Unverified
FedZKP: Federated Model Ownership Verification with Zero-knowledge Proof	May 8, 2023	Federated Learning	—Unverified
FedZKT: Zero-Shot Knowledge Transfer towards Resource-Constrained Federated Learning with Heterogeneous On-Device Models	Sep 8, 2021	Federated LearningTransfer Learning	—Unverified
Fed-ZOE: Communication-Efficient Over-the-Air Federated Learning via Zeroth-Order Estimation	Dec 21, 2024	Computational EfficiencyFederated Learning	—Unverified
FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning	Jun 7, 2022	Ensemble LearningFederated Learning	—Unverified
Federated Learning with Heterogeneous Differential Privacy	Oct 28, 2021	Federated LearningPrivacy Preserving	—Unverified
FetchSGD: Communication-Efficient Federated Learning with Sketching	Jul 15, 2020	Federated Learning	—Unverified
FEVERLESS: Fast and Secure Vertical Federated Learning based on XGBoost for Decentralized Labels	Sep 29, 2021	Federated LearningVertical Federated Learning	—Unverified
FewFedWeight: Few-shot Federated Learning Framework across Multiple NLP Tasks	Dec 16, 2022	Federated LearningFew-Shot Learning	—Unverified
Few-Round Learning for Federated Learning	Jan 1, 2021	Federated LearningFew-Shot Learning	—Unverified
Few-Shot Generation of Brain Tumors for Secure and Fair Data Sharing	Mar 31, 2025	Brain Tumor SegmentationData Augmentation	—Unverified
FGAD: Self-boosted Knowledge Distillation for An Effective Federated Graph Anomaly Detection Framework	Feb 20, 2024	Anomaly DetectionFederated Learning	—Unverified
FGLP: A Federated Fine-Grained Location Prediction System for Mobile Users	Jun 13, 2021	Federated LearningPrediction	—Unverified
FheFL: Fully Homomorphic Encryption Friendly Privacy-Preserving Federated Learning with Byzantine Users	Jun 8, 2023	Data PoisoningFederated Learning	—Unverified
Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devices	Oct 21, 2023	Data AugmentationFederated Learning	—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