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 2076–2100 of 6771 papers

Title	Date	Tasks	Status
A GAN-based data poisoning framework against anomaly detection in vertical federated learning	Jan 17, 2024	Anomaly DetectionData Poisoning	—Unverified
Covert Communication Based on the Poisoning Attack in Federated Learning	Jun 2, 2023	Computer SecurityFederated Learning	—Unverified
Turning Federated Learning Systems Into Covert Channels	Apr 21, 2021	Federated LearningModel Poisoning	—Unverified
Asynchronous Online Federated Learning for Edge Devices with Non-IID Data	Nov 5, 2019	Federated Learning	—Unverified
Asynchronous Multi-Server Federated Learning for Geo-Distributed Clients	Jun 3, 2024	Federated Learningimage-classification	—Unverified
Cost-Effective Federated Learning in Mobile Edge Networks	Sep 12, 2021	Federated Learning	—Unverified
A Game-theoretic Framework for Privacy-preserving Federated Learning	Apr 11, 2023	Federated LearningPrivacy Preserving	—Unverified
Adaptive Client Selection in Federated Learning: A Network Anomaly Detection Use Case	Jan 25, 2025	Anomaly DetectionFederated Learning	—Unverified
FDA3 : Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications	Jun 28, 2020	Federated Learning	—Unverified
Cost-Effective Federated Learning Design	Dec 15, 2020	Federated Learning	—Unverified
CosSGD: Communication-Efficient Federated Learning with a Simple Cosine-Based Quantization	Dec 15, 2020	Federated Learningimage-classification	—Unverified
Asynchronous Multi-Model Dynamic Federated Learning over Wireless Networks: Theory, Modeling, and Optimization	May 22, 2023	Federated LearningScheduling	—Unverified
Accelerating Hybrid Federated Learning Convergence under Partial Participation	Apr 10, 2023	Federated Learning	—Unverified
Asynchronous Local Computations in Distributed Bayesian Learning	Nov 6, 2023	Federated Learning	—Unverified
Local SGD Accelerates Convergence by Exploiting Second Order Information of the Loss Function	May 24, 2023	Federated Learning	—Unverified
FBChain: A Blockchain-based Federated Learning Model with Efficiency and Secure Communication	Nov 21, 2023	Federated Learning	—Unverified
Correlation Aware Sparsified Mean Estimation Using Random Projection	Oct 29, 2023	Distributed OptimizationFederated Learning	—Unverified
Correlated Privacy Mechanisms for Differentially Private Distributed Mean Estimation	Jul 3, 2024	Federated LearningPrivacy Preserving	—Unverified
Asynchronous Hierarchical Federated Learning	May 31, 2022	Federated Learningimage-classification	—Unverified
Corrected with the Latest Version: Make Robust Asynchronous Federated Learning Possible	Apr 5, 2025	Federated LearningKnowledge Distillation	—Unverified
Asynchronous Federated Stochastic Optimization for Heterogeneous Objectives Under Arbitrary Delays	May 16, 2024	Federated LearningStochastic Optimization	—Unverified
A Framework for Privacy-Preserving in IoV Using Federated Learning With Differential Privacy	Dec 9, 2024	Federated LearningPrivacy Preserving	—Unverified
A Privacy-Preserving Federated Learning Framework for Generalizable CBCT to Synthetic CT Translation in Head and Neck	Jun 10, 2025	Federated LearningGenerative Adversarial Network	—Unverified
FCNCP: A Coupled Nonnegative CANDECOMP/PARAFAC Decomposition Based on Federated Learning	Apr 18, 2024	EEGERP	—Unverified
FDNAS: Improving Data Privacy and Model Diversity in AutoML	Nov 6, 2020	AutoMLDiversity	—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