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 4301–4325 of 6771 papers

Title	Date	Tasks	Status
FedMinds: Privacy-Preserving Personalized Brain Visual Decoding	Sep 3, 2024	Federated LearningPrivacy Preserving	—Unverified
FedMint: Intelligent Bilateral Client Selection in Federated Learning with Newcomer IoT Devices	Oct 31, 2022	Federated LearningPrivacy Preserving	—Unverified
Fed-MIWAE: Federated Imputation of Incomplete Data via Deep Generative Models	Apr 17, 2023	Federated LearningImputation	—Unverified
FedMKGC: Privacy-Preserving Federated Multilingual Knowledge Graph Completion	Dec 17, 2023	Entity AlignmentFederated Learning	—Unverified
FedMM: Federated Multi-Modal Learning with Modality Heterogeneity in Computational Pathology	Feb 24, 2024	Federated LearningPrivacy Preserving	—Unverified
FedMM-X: A Trustworthy and Interpretable Framework for Federated Multi-Modal Learning in Dynamic Environments	Mar 25, 2025	Federated LearningOut-of-Distribution Generalization	—Unverified
FedMobile: Enabling Knowledge Contribution-aware Multi-modal Federated Learning with Incomplete Modalities	Feb 20, 2025	Federated LearningTransfer Learning	—Unverified
FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation	Nov 4, 2024	Federated LearningMixture-of-Experts	—Unverified
FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts	Aug 21, 2024	Federated LearningHeuristic Search	—Unverified
FedMorph: Communication Efficient Federated Learning via Morphing Neural Network	Sep 29, 2021	Federated LearningMORPH	—Unverified
FedMPQ: Secure and Communication-Efficient Federated Learning with Multi-codebook Product Quantization	Apr 21, 2024	Federated LearningQuantization	—Unverified
Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombination	May 18, 2023	Federated Learning	—Unverified
FedMR: Fedreated Learning via Model Recombination	Aug 16, 2022	Federated Learningmodel	—Unverified
FedMS: Federated Learning with Mixture of Sparsely Activated Foundations Models	Dec 26, 2023	Edge-computingFederated Learning	—Unverified
FedMSGL: A Self-Expressive Hypergraph Based Federated Multi-View Learning	Mar 12, 2025	Federated LearningMULTI-VIEW LEARNING	—Unverified
FedMT: Federated Learning with Mixed-type Labels	Oct 5, 2022	Federated LearningVocal Bursts Type Prediction	—Unverified
FedNano: Toward Lightweight Federated Tuning for Pretrained Multimodal Large Language Models	Jun 12, 2025	Cross-Modal RetrievalFederated Learning	—Unverified
FedNAS: Federated Deep Learning via Neural Architecture Search	Sep 29, 2021	Deep LearningFederated Learning	—Unverified
FedNC: A Secure and Efficient Federated Learning Method with Network Coding	May 5, 2023	Federated Learning	—Unverified
Fed-NDIF: A Noise-Embedded Federated Diffusion Model For Low-Count Whole-Body PET Denoising	Mar 20, 2025	DenoisingFederated Learning	—Unverified
FedNER: Privacy-preserving Medical Named Entity Recognition with Federated Learning	Mar 20, 2020	Federated LearningMedical Named Entity Recognition	—Unverified
FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction	Sep 17, 2024	Dimensionality ReductionFederated Learning	—Unverified
FedNet2Net: Saving Communication and Computations in Federated Learning with Model Growing	Jul 19, 2022	Federated Learning	—Unverified
FedNIA: Noise-Induced Activation Analysis for Mitigating Data Poisoning in FL	Feb 23, 2025	Data PoisoningFederated Learning	—Unverified
FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction	Dec 19, 2021	Disease PredictionFederated 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