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 2101–2125 of 6771 papers

Title	Date	Tasks	Status
An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning	Nov 13, 2021	Anomaly DetectionFederated Learning	—Unverified
Efficient and Private Federated Learning with Partially Trainable Networks	Oct 6, 2021	Federated Learning	—Unverified
Efficient and Privacy Preserving Group Signature for Federated Learning	Jul 12, 2022	Federated LearningPrivacy Preserving	—Unverified
Bristle: Decentralized Federated Learning in Byzantine, Non-i.i.d. Environments	Oct 21, 2021	Federated LearningTransfer Learning	—Unverified
Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout	Oct 28, 2022	Federated Learning	—Unverified
MUDGUARD: Taming Malicious Majorities in Federated Learning using Privacy-Preserving Byzantine-Robust Clustering	Aug 22, 2022	ClusteringFederated Learning	—Unverified
An Energy and Carbon Footprint Analysis of Distributed and Federated Learning	Jun 21, 2022	Federated Learning	—Unverified
A Client-level Assessment of Collaborative Backdoor Poisoning in Non-IID Federated Learning	Apr 17, 2025	Federated LearningModel Poisoning	—Unverified
Abnormal Local Clustering in Federated Learning	Aug 26, 2022	ClusteringFederated Learning	—Unverified
Efficient Adaptive Federated Optimization of Federated Learning for IoT	Jun 23, 2022	Federated LearningPrivacy Preserving	—Unverified
Efficient Adaptive Federated Optimization	Oct 10, 2024	Federated Learning	—Unverified
Bridging the Gap Between Foundation Models and Heterogeneous Federated Learning	Sep 30, 2023	Federated LearningModel Compression	—Unverified
EFFGAN: Ensembles of fine-tuned federated GANs	Jun 23, 2022	Federated Learning	—Unverified
Effect of Homomorphic Encryption on the Performance of Training Federated Learning Generative Adversarial Networks	Jul 1, 2022	Federated LearningGenerative Adversarial Network	—Unverified
An End-to-End Encrypted Neural Network for Gradient Updates Transmission in Federated Learning	Aug 22, 2019	Data CompressionFederated Learning	—Unverified
Effectively Heterogeneous Federated Learning: A Pairing and Split Learning Based Approach	Aug 26, 2023	Federated LearningPrivacy Preserving	—Unverified
Bridging Data Islands: Geographic Heterogeneity-Aware Federated Learning for Collaborative Remote Sensing Semantic Segmentation	Apr 14, 2024	Earth ObservationFederated Learning	—Unverified
Effective Federated Adaptive Gradient Methods with Non-IID Decentralized Data	Sep 14, 2020	Edge-computingFederated Learning	—Unverified
Bridging Data Barriers among Participants: Assessing the Potential of Geoenergy through Federated Learning	Apr 29, 2024	Bayesian OptimizationBinary Classification	—Unverified
An Empirical Study of Vulnerability Detection using Federated Learning	Nov 25, 2024	Federated LearningVulnerability Detection	—Unverified
A decentralized aggregation mechanism for training deep learning models using smart contract system for bank loan prediction	Nov 22, 2020	Federated Learning	—Unverified
Effective and secure federated online learning to rank	Dec 26, 2024	Federated LearningLearning-To-Rank	—Unverified
Effective and Efficient Federated Tree Learning on Hybrid Data	Oct 18, 2023	Federated Learning	—Unverified
BRFL: A Blockchain-based Byzantine-Robust Federated Learning Model	Oct 20, 2023	Federated Learning	—Unverified
Effective and Efficient Cross-City Traffic Knowledge Transfer: A Privacy-Preserving Perspective	Mar 15, 2025	Federated LearningImputation	—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