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 3326–3350 of 6771 papers

Title	Date	Tasks	Status
FedLite: A Scalable Approach for Federated Learning on Resource-constrained Clients	Jan 28, 2022	Federated Learning	—Unverified
Federated Learning for Computer Vision	Aug 24, 2023	Federated Learning	—Unverified
Federated Learning for Computationally-Constrained Heterogeneous Devices: A Survey	Jul 18, 2023	Federated LearningPrivacy Preserving	—Unverified
Digital Over-the-Air Federated Learning in Multi-Antenna Systems	Feb 4, 2023	Federated Learning	—Unverified
Federated Neuro-Symbolic Learning	Aug 29, 2023	Bilevel OptimizationFederated Learning	—Unverified
FedLog: Personalized Federated Classification with Less Communication and More Flexibility	Jul 11, 2024	Bayesian InferenceFederated Learning	—Unverified
pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning	Oct 20, 2023	Federated LearningPersonalized Federated Learning	—Unverified
Federated Learning for Commercial Image Sources	Jul 17, 2025	Federated Learningimage-classification	—Unverified
Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives	Apr 17, 2023	Federated LearningLoad Forecasting	—Unverified
A Systematic Decade Review of Trip Route Planning with Travel Time Estimation based on User Preferences and Behavior	Mar 30, 2025	Data IntegrationFederated Learning	—Unverified
Heterogeneous Federated Learning with Splited Language Model	Mar 24, 2024	Federated LearningLanguage Modeling	—Unverified
Adaptive Coded Federated Learning: Privacy Preservation and Straggler Mitigation	Mar 22, 2024	Federated Learning	—Unverified
FedMABA: Towards Fair Federated Learning through Multi-Armed Bandits Allocation	Oct 26, 2024	FairnessFederated Learning	—Unverified
Federated Learning for Coalition Operations	Oct 14, 2019	BIG-bench Machine LearningFederated Learning	—Unverified
FedMAC: Tackling Partial-Modality Missing in Federated Learning with Cross-Modal Aggregation and Contrastive Regularization	Oct 4, 2024	Federated Learning	—Unverified
FedMAE: Federated Self-Supervised Learning with One-Block Masked Auto-Encoder	Mar 20, 2023	Federated LearningImage Reconstruction	—Unverified
FedMap: Iterative Magnitude-Based Pruning for Communication-Efficient Federated Learning	Jun 27, 2024	Federated Learning	—Unverified
Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches	Nov 6, 2023	Federated LearningPrivacy Preserving	—Unverified
Cross-Fusion Rule for Personalized Federated Learning	Feb 6, 2023	Federated LearningPersonalized Federated Learning	—Unverified
Federated Learning for Chronic Obstructive Pulmonary Disease Classification with Partial Personalized Attention Mechanism	Oct 28, 2022	Federated LearningPersonalized Federated Learning	—Unverified
FedMCP: Parameter-Efficient Federated Learning with Model-Contrastive Personalization	Aug 28, 2024	Federated Learningparameter-efficient fine-tuning	—Unverified
Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO	Aug 25, 2020	Federated Learning	—Unverified
Cross-domain Federated Object Detection	Jun 30, 2022	Autonomous DrivingFederated Learning	—Unverified
AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization	Sep 26, 2021	Federated LearningPrivacy Preserving	—Unverified
Federated Learning for Cellular-connected UAVs: Radio Mapping and Path Planning	Aug 23, 2020	Federated Learning	—Unverified

Show:10 25 50

← PrevPage 134 of 271Next →

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