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 851–875 of 6771 papers

Title	Date	Tasks	Status
Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach	Jan 22, 2025	Anomaly DetectionDimensionality Reduction	—Unverified
A Comparative Evaluation of FedAvg and Per-FedAvg Algorithms for Dirichlet Distributed Heterogeneous Data	Sep 3, 2023	Federated Learning	—Unverified
Load Balancing in Federated Learning	Aug 1, 2024	Decision MakingFairness	—Unverified
Anomalous Client Detection in Federated Learning	Nov 3, 2024	Anomaly DetectionEdge-computing	—Unverified
AnoFel: Supporting Anonymity for Privacy-Preserving Federated Learning	Jun 12, 2023	Federated LearningPrivacy Preserving	—Unverified
Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detection	Mar 30, 2025	DiagnosticFederated Learning	—Unverified
FLBench: A Benchmark Suite for Federated Learning	Aug 17, 2020	BIG-bench Machine LearningFederated Learning	—Unverified
An Investigation towards Differentially Private Sequence Tagging in a Federated Framework	Jun 1, 2021	Federated Learningnamed-entity-recognition	—Unverified
How global observation works in Federated Learning: Integrating vertical training into Horizontal Federated Learning	Dec 2, 2021	Federated Learning	—Unverified
A cautionary tale on the cost-effectiveness of collaborative AI in real-world medical applications	Dec 9, 2024	Federated Learning	—Unverified
CDKT-FL: Cross-Device Knowledge Transfer using Proxy Dataset in Federated Learning	Apr 4, 2022	Federated LearningKnowledge Distillation	—Unverified
An Interpretable Federated Learning-based Network Intrusion Detection Framework	Jan 10, 2022	Federated LearningIntrusion Detection	—Unverified
AdRo-FL: Informed and Secure Client Selection for Federated Learning in the Presence of Adversarial Aggregator	Jun 21, 2025	Federated LearningPrivacy Preserving	—Unverified
Conditioning on Local Statistics for Scalable Heterogeneous Federated Learning	Mar 1, 2025	Federated Learning	—Unverified
A(DP)^2SGD: Asynchronous Decentralized Parallel Stochastic Gradient Descent with Differential Privacy	Aug 21, 2020	Federated Learning	—Unverified
An Intelligent Native Network Slicing Security Architecture Empowered by Federated Learning	Oct 4, 2024	Federated Learning	—Unverified
A Carbon Tracking Model for Federated Learning: Impact of Quantization and Sparsification	Oct 12, 2023	Federated LearningQuantization	—Unverified
CD2-pFed: Cyclic Distillation-Guided Channel Decoupling for Model Personalization in Federated Learning	Jan 1, 2022	Federated LearningMedical Image Analysis	—Unverified
An Intelligent Mechanism for Monitoring and Detecting Intrusions in IoT Devices	Jun 23, 2023	Federated LearningIntrusion Detection	—Unverified
An Innovative Networks in Federated Learning	May 28, 2024	Computational EfficiencyFederated Learning	—Unverified
ADMarker: A Multi-Modal Federated Learning System for Monitoring Digital Biomarkers of Alzheimer's Disease	Oct 23, 2023	Federated LearningPrivacy Preserving	—Unverified
An Information Theoretic Perspective on Conformal Prediction	May 3, 2024	Conformal PredictionFederated Learning	—Unverified
An Information-Theoretic Analysis of The Cost of Decentralization for Learning and Inference Under Privacy Constraints	Oct 11, 2021	Federated LearningVertical Federated Learning	—Unverified
A Communication-Efficient Adaptive Algorithm for Federated Learning under Cumulative Regret	Jan 21, 2023	Federated LearningStochastic Optimization	—Unverified
CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clustering	Jul 17, 2024	Activity RecognitionClustering	—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