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 2751–2775 of 6771 papers

Title	Date	Tasks	Status
FedCostWAvg: A new averaging for better Federated Learning	Nov 16, 2021	Federated LearningSegmentation	—Unverified
Federated Learning for UAV Swarms Under Class Imbalance and Power Consumption Constraints	Aug 23, 2021	Federated Learning	—Unverified
Federated Learning for Ultra-Reliable Low-Latency V2V Communications	May 11, 2018	Federated Learning	—Unverified
Federated Learning in Vehicular Networks	Jun 2, 2020	Autonomous DrivingFederated Learning	—Unverified
Communication-Efficient Federated Learning for Neural Machine Translation	Dec 12, 2021	Federated LearningMachine Translation	—Unverified
Federated Learning for Water Consumption Forecasting in Smart Cities	Jan 30, 2023	Federated Learning	—Unverified
Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges	Jul 30, 2019	Federated LearningPrivacy Preserving	—Unverified
Federated Learning for Zero-Day Attack Detection in 5G and Beyond V2X Networks	Jul 3, 2024	Federated LearningIntrusion Detection	—Unverified
FEDERATED LEARNING FRAMEWORK BASED ON TRIMMED MEAN AGGREGATION RULES	Sep 29, 2021	Federated Learning	—Unverified
A Safe Deep Reinforcement Learning Approach for Energy Efficient Federated Learning in Wireless Communication Networks	Aug 21, 2023	Deep Reinforcement LearningFederated Learning	—Unverified
Data-Free Black-Box Federated Learning via Zeroth-Order Gradient Estimation	Mar 8, 2025	Federated Learningimage-classification	—Unverified
Federated Learning framework for LoRaWAN-enabled IIoT communication: A case study	Oct 15, 2024	Anomaly DetectionFederated Learning	—Unverified
Active Federated Learning	Sep 27, 2019	Federated Learning	—Unverified
Data-Free Distillation Improves Efficiency and Privacy in Federated Thorax Disease Analysis	Oct 22, 2023	Federated LearningPrivacy Preserving	—Unverified
Federated Learning from Heterogeneous Data via Controlled Bayesian Air Aggregation	Mar 30, 2023	Federated Learning	—Unverified
Federated Learning from Molecules to Processes: A Perspective	Jun 23, 2025	Federated Learning	—Unverified
Communication-Efficient Federated Learning via Quantized Compressed Sensing	Nov 30, 2021	compressed sensingFederated Learning	—Unverified
Federated Learning from Pre-Trained Models: A Contrastive Learning Approach	Sep 21, 2022	Contrastive LearningFederated Learning	—Unverified
Model Splitting Enhanced Communication-Efficient Federated Learning for CSI Feedback	Jun 4, 2025	Federated Learning	—Unverified
Federated Learning: From Theory to Practice	May 25, 2025	Distributed OptimizationFederated Learning	—Unverified
Communication-Efficient Federated Learning via Optimal Client Sampling	Jul 30, 2020	Federated LearningLanguage Modeling	—Unverified
Artificial Intelligence in Pediatric Echocardiography: Exploring Challenges, Opportunities, and Clinical Applications with Explainable AI and Federated Learning	Nov 15, 2024	Decision MakingDiagnostic	—Unverified
A Two-Stage Federated Learning Approach for Industrial Prognostics Using Large-Scale High-Dimensional Signals	Oct 14, 2024	Dimensionality ReductionFederated Learning	—Unverified
Federated Learning in Adversarial Environments: Testbed Design and Poisoning Resilience in Cybersecurity	Sep 15, 2024	Federated LearningIntrusion Detection	—Unverified
Federated Learning over Noisy Channels: Convergence Analysis and Design Examples	Jan 6, 2021	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