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 901–925 of 6771 papers

Title	Date	Tasks	Status	Score
Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated Learning	Nov 29, 2023	Federated LearningInference Attack	CodeCode Available	5
FEDKIM: Adaptive Federated Knowledge Injection into Medical Foundation Models	Aug 17, 2024	Federated LearningMixture-of-Experts	CodeCode Available	5
FedHarmony: Unlearning Scanner Bias with Distributed Data	May 31, 2022	Federated Learning	CodeCode Available	5
An Element-Wise Weights Aggregation Method for Federated Learning	Apr 24, 2024	Federated Learning	CodeCode Available	5
FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations	Feb 2, 2023	Federated Learningglobal-optimization	CodeCode Available	5
FedGrad: Mitigating Backdoor Attacks in Federated Learning Through Local Ultimate Gradients Inspection	Apr 29, 2023	Backdoor AttackFederated Learning	CodeCode Available	5
FedFOR: Stateless Heterogeneous Federated Learning with First-Order Regularization	Sep 21, 2022	Federated Learning	CodeCode Available	5
Achieving Model Fairness in Vertical Federated Learning	Sep 17, 2021	BIG-bench Machine LearningFairness	CodeCode Available	5
FedFT: Improving Communication Performance for Federated Learning with Frequency Space Transformation	Sep 8, 2024	Federated Learning	CodeCode Available	5
FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation	Sep 30, 2023	Federated Learning	CodeCode Available	5
BOBA: Byzantine-Robust Federated Learning with Label Skewness	Aug 27, 2022	Federated LearningSelection bias	CodeCode Available	5
Addressing Heterogeneity in Federated Learning via Distributional Transformation	Oct 26, 2022	Data AugmentationFederated Learning	CodeCode Available	5
FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET Denoising	Apr 2, 2023	DenoisingFederated Learning	CodeCode Available	5
FedExP: Speeding Up Federated Averaging via Extrapolation	Jan 23, 2023	Federated Learning	CodeCode Available	5
Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection	May 27, 2025	DecompensationFederated Learning	CodeCode Available	5
Federating Dynamic Models using Early-Exit Architectures for Automatic Speech Recognition on Heterogeneous Clients	May 27, 2024	Automatic Speech RecognitionFederated Learning	CodeCode Available	5
Federated User Preference Modeling for Privacy-Preserving Cross-Domain Recommendation	Aug 26, 2024	Federated LearningPrivacy Preserving	CodeCode Available	5
Buffer-based Gradient Projection for Continual Federated Learning	Sep 3, 2024	Federated LearningIncremental Learning	CodeCode Available	5
Federated Visual Classification with Real-World Data Distribution	Mar 18, 2020	ClassificationDiversity	CodeCode Available	5
Federated Unlearning via Class-Discriminative Pruning	Oct 22, 2021	Federated Learningimage-classification	CodeCode Available	5
Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate Gradients	Aug 8, 2023	Adversarial AttackFederated Learning	CodeCode Available	5
Federated Two Stage Decoupling With Adaptive Personalization Layers	Aug 30, 2023	ClusteringFederated Learning	CodeCode Available	5
A Client-server Deep Federated Learning for Cross-domain Surgical Image Segmentation	Jun 14, 2023	DiagnosticDomain Adaptation	CodeCode Available	5
An effective and efficient green federated learning method for one-layer neural networks	Dec 22, 2023	Edge-computingFederated Learning	CodeCode Available	5
Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions	Mar 1, 2024	FairnessFederated Learning	CodeCode Available	5

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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