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 601–625 of 6771 papers

Title	Date	Tasks	Status	Hype
FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise	Dec 20, 2023	Federated LearningImage Segmentation	CodeCode Available	1
FedAdapt: Adaptive Offloading for IoT Devices in Federated Learning	Jul 9, 2021	Federated Learning	CodeCode Available	1
FedAS: Bridging Inconsistency in Personalized Federated Learning	Jan 1, 2024	Federated LearningPersonalized Federated Learning	CodeCode Available	1
Exploring Federated Unlearning: Review, Comparison, and Insights	Oct 30, 2023	Federated LearningPrivacy Preserving	CodeCode Available	1
FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning	Feb 4, 2021	Federated LearningUnsupervised Pre-training	CodeCode Available	1
FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors	Mar 20, 2025	Federated Learningglobal-optimization	CodeCode Available	1
Benchmarking Algorithms for Federated Domain Generalization	Jul 11, 2023	BenchmarkingDiversity	CodeCode Available	1
Beyond Traditional Threats: A Persistent Backdoor Attack on Federated Learning	Apr 26, 2024	Backdoor AttackFederated Learning	CodeCode Available	1
FedCFA: Alleviating Simpson's Paradox in Model Aggregation with Counterfactual Federated Learning	Dec 25, 2024	counterfactualDistributed Optimization	CodeCode Available	1
FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural Networks	Oct 25, 2022	Federated LearningPersonalized Federated Learning	CodeCode Available	1
FedCM: Federated Learning with Client-level Momentum	Jun 21, 2021	Federated Learning	CodeCode Available	1
FedCMR: Federated Cross-Modal Retrieval	Jul 1, 2021	Cross-Modal RetrievalFederated Learning	CodeCode Available	1
FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler	Sep 26, 2023	Federated Learning	CodeCode Available	1
FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated Clients	Feb 28, 2025	Federated Learning	CodeCode Available	1
FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks	Nov 22, 2021	BenchmarkingFederated Learning	CodeCode Available	1
FedDANE: A Federated Newton-Type Method	Jan 7, 2020	Distributed OptimizationFederated Learning	CodeCode Available	1
FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction	Mar 22, 2022	Federated Learningimage-classification	CodeCode Available	1
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning	Aug 23, 2021	Federated LearningMisconceptions	CodeCode Available	1
FedDefender: Backdoor Attack Defense in Federated Learning	Jul 2, 2023	Backdoor AttackData Poisoning	CodeCode Available	1
FedDefender: Client-Side Attack-Tolerant Federated Learning	Jul 18, 2023	Federated LearningKnowledge Distillation	CodeCode Available	1
FedDIP: Federated Learning with Extreme Dynamic Pruning and Incremental Regularization	Sep 13, 2023	Federated Learning	CodeCode Available	1
FedDisco: Federated Learning with Discrepancy-Aware Collaboration	May 30, 2023	Federated Learning	CodeCode Available	1
Asynchronous Federated Learning for Edge-assisted Vehicular Networks	Aug 3, 2022	Federated Learning	CodeCode Available	1
FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphs	Aug 29, 2022	Federated LearningGraph Learning	CodeCode Available	1
ARFED: Attack-Resistant Federated averaging based on outlier elimination	Nov 8, 2021	Data PoisoningFederated Learning	CodeCode Available	1

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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