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 3626–3650 of 6771 papers

Title	Date	Tasks	Status
Federated Learning in UAV-Enhanced Networks: Joint Coverage and Convergence Time Optimization	Aug 31, 2023	Federated Learning	—Unverified
FTA: Stealthy and Adaptive Backdoor Attack with Flexible Triggers on Federated Learning	Aug 31, 2023	Backdoor AttackFederated Learning	—Unverified
Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training	Aug 31, 2023	Federated LearningLesion Segmentation	—Unverified
Robust Networked Federated Learning for Localization	Aug 31, 2023	Computational EfficiencyFederated Learning	—Unverified
FedDD: Toward Communication-efficient Federated Learning with Differential Parameter Dropout	Aug 31, 2023	Federated Learning	—Unverified
Communication-Efficient Decentralized Federated Learning via One-Bit Compressive Sensing	Aug 31, 2023	Compressive SensingComputational Efficiency	—Unverified
Learning Driver Models for Automated Vehicles via Knowledge Sharing and Personalization	Aug 31, 2023	Federated LearningManagement	—Unverified
Federated Two Stage Decoupling With Adaptive Personalization Layers	Aug 30, 2023	ClusteringFederated Learning	CodeCode Available
FedCiR: Client-Invariant Representation Learning for Federated Non-IID Features	Aug 30, 2023	Federated LearningRepresentation Learning	—Unverified
Efficient Model Personalization in Federated Learning via Client-Specific Prompt Generation	Aug 29, 2023	Federated Learning	—Unverified
Federated Neuro-Symbolic Learning	Aug 29, 2023	Bilevel OptimizationFederated Learning	—Unverified
Adversarial Predictions of Data Distributions Across Federated Internet-of-Things Devices	Aug 28, 2023	Federated Learning	—Unverified
Generalizable Learning Reconstruction for Accelerating MR Imaging via Federated Neural Architecture Search	Aug 27, 2023	Efficient Neural NetworkFairness	CodeCode Available
Revolutionizing Disease Diagnosis: A Microservices-Based Architecture for Privacy-Preserving and Efficient IoT Data Analytics Using Federated Learning	Aug 27, 2023	Distributed ComputingFederated Learning	—Unverified
Uncovering Promises and Challenges of Federated Learning to Detect Cardiovascular Diseases: A Scoping Literature Review	Aug 26, 2023	Federated LearningSurvey	—Unverified
Effectively Heterogeneous Federated Learning: A Pairing and Split Learning Based Approach	Aug 26, 2023	Federated LearningPrivacy Preserving	—Unverified
FAM: fast adaptive federated meta-learning	Aug 26, 2023	Federated LearningMeta-Learning	—Unverified
Price-Discrimination Game for Distributed Resource Management in Federated Learning	Aug 26, 2023	Federated LearningManagement	—Unverified
Heterogeneous Federated Learning via Personalized Generative Networks	Aug 25, 2023	Federated LearningTransfer Learning	—Unverified
REFT: Resource-Efficient Federated Training Framework for Heterogeneous and Resource-Constrained Environments	Aug 25, 2023	Federated Learningimage-classification	—Unverified
Federated Learning in IoT: a Survey from a Resource-Constrained Perspective	Aug 25, 2023	Decision MakingFederated Learning	—Unverified
DAG-ACFL: Asynchronous Clustered Federated Learning based on DAG-DLT	Aug 25, 2023	Change Point DetectionFederated Learning	—Unverified
Federated Causal Inference from Observational Data	Aug 24, 2023	Causal InferenceFederated Learning	CodeCode Available
LR-XFL: Logical Reasoning-based Explainable Federated Learning	Aug 24, 2023	Federated LearningLogical Reasoning	CodeCode Available
A Huber Loss Minimization Approach to Byzantine Robust Federated Learning	Aug 24, 2023	Federated 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