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 3101–3125 of 6771 papers

Title	Date	Tasks	Status
Federated Self-Supervised Learning for One-Shot Cross-Modal and Cross-Imaging Technique Segmentation	Mar 30, 2025	Federated LearningImage Segmentation	—Unverified
Federated Learning for Medical Image Analysis: A Survey	Jun 9, 2023	Federated LearningMedical Image Analysis	—Unverified
DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning	Oct 31, 2021	Federated LearningPrivacy Preserving	—Unverified
Federated Self-Supervised Learning of Monocular Depth Estimators for Autonomous Vehicles	Oct 7, 2023	Autonomous VehiclesDepth Estimation	—Unverified
DACFL: Dynamic Average Consensus Based Federated Learning in Decentralized Topology	Nov 10, 2021	Federated Learning	—Unverified
Federated Self-supervised Speech Representations: Are We There Yet?	Apr 6, 2022	Federated LearningSelf-Supervised Learning	—Unverified
Adaptive Decentralized Federated Learning in Energy and Latency Constrained Wireless Networks	Mar 29, 2024	Federated Learning	—Unverified
Accelerating Split Federated Learning over Wireless Communication Networks	Oct 24, 2023	Federated Learning	—Unverified
Federated Learning for Medical Image Classification: A Comprehensive Benchmark	Apr 7, 2025	Computational EfficiencyDenoising	—Unverified
Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan	Nov 23, 2020	Federated LearningManagement	—Unverified
Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions	Aug 5, 2022	DiagnosticFederated Learning	—Unverified
DABS: Data-Agnostic Backdoor attack at the Server in Federated Learning	May 2, 2023	Backdoor AttackFederated Learning	—Unverified
Federated Learning for Localization: A Privacy-Preserving Crowdsourcing Method	Jan 7, 2020	Federated LearningPrivacy Preserving	—Unverified
D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data	Jul 15, 2025	Federated LearningTime Series	—Unverified
Federated Sequence-to-Sequence Learning for Load Disaggregation from Unbalanced Low-Resolution Smart Meter Data	Aug 15, 2024	Federated LearningNon-Intrusive Load Monitoring	—Unverified
Federated Short-Term Load Forecasting with Personalization Layers for Heterogeneous Clients	Sep 22, 2023	Federated LearningLoad Forecasting	—Unverified
ATPFL: Automatic Trajectory Prediction Model Design Under Federated Learning Framework	Jan 1, 2022	Federated LearningTrajectory Prediction	—Unverified
Federated Sinkhorn	Feb 10, 2025	Federated Learning	—Unverified
Federated Learning for Large-Scale Scene Modeling with Neural Radiance Fields	Sep 12, 2023	Federated LearningLifelong learning	—Unverified
Federated Smoothing Proximal Gradient for Quantile Regression with Non-Convex Penalties	Aug 10, 2024	Federated Learningquantile regression	—Unverified
Federated Social Recommendation with Graph Neural Network	Nov 21, 2021	Federated LearningGraph Neural Network	—Unverified
Federated Source-free Domain Adaptation for Classification: Weighted Cluster Aggregation for Unlabeled Data	Dec 18, 2024	Domain AdaptationFederated Learning	—Unverified
D2p-fed:Differentially Private Federated Learning with Efficient Communication	Jan 1, 2021	Federated Learning	—Unverified
Federated Learning for IoUT: Concepts, Applications, Challenges and Opportunities	Jul 28, 2022	BIG-bench Machine LearningFederated Learning	—Unverified
Federated Learning for Intrusion Detection in IoT Security: A Hybrid Ensemble Approach	Jun 25, 2021	Federated LearningIntrusion Detection	—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