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 251–275 of 6771 papers

Title	Date	Tasks	Status	Hype	Score
Differentially private cross-silo federated learning	Jul 10, 2020	Federated Learning	CodeCode Available	1	5
Differentially Private Federated Learning on Heterogeneous Data	Nov 17, 2021	Federated Learning	CodeCode Available	1	5
Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence	Nov 18, 2021	COVID-19 DiagnosisDiagnostic	CodeCode Available	1	5
Communication Efficient Federated Learning for Multilingual Neural Machine Translation with Adapter	May 21, 2023	ClusteringFederated Learning	CodeCode Available	1	5
APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning	Feb 8, 2022	Federated LearningPrivacy Preserving	CodeCode Available	1	5
Detecting Backdoor Attacks in Federated Learning via Direction Alignment Inspection	Mar 11, 2025	Federated Learning	CodeCode Available	1	5
APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service	Aug 17, 2023	Federated LearningPrivacy Preserving	CodeCode Available	1	5
ACTION: Augmentation and Computation Toolbox for Brain Network Analysis with Functional MRI	May 10, 2024	Data AugmentationFederated Learning	CodeCode Available	1	5
A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection	Feb 7, 2023	Federated LearningPrivacy Preserving	CodeCode Available	1	5
Device Heterogeneity in Federated Learning: A Superquantile Approach	Feb 25, 2020	Federated Learning	CodeCode Available	1	5
Differentially Private Learning with Adaptive Clipping	May 9, 2019	Federated Learning	CodeCode Available	1	5
Dual-Personalizing Adapter for Federated Foundation Models	Mar 28, 2024	Federated Learning	CodeCode Available	1	5
Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank Decomposition	Jun 28, 2024	Federated LearningGeneral Knowledge	CodeCode Available	1	5
Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition	Oct 26, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	1	5
Deep Federated Learning for Autonomous Driving	Oct 12, 2021	Autonomous DrivingFederated Learning	CodeCode Available	1	5
Decentralized Federated Learning: Balancing Communication and Computing Costs	Jul 26, 2021	Federated Learning	CodeCode Available	1	5
Anomaly-Flow: A Multi-domain Federated Generative Adversarial Network for Distributed Denial-of-Service Detection	Mar 18, 2025	Federated LearningGenerative Adversarial Network	CodeCode Available	1	5
Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges	Nov 15, 2022	Federated Learning	CodeCode Available	1	5
DeceFL: A Principled Decentralized Federated Learning Framework	Jul 15, 2021	Federated LearningPrivacy Preserving	CodeCode Available	1	5
An Empirical Study of Personalized Federated Learning	Jun 27, 2022	BIG-bench Machine LearningFederated Learning	CodeCode Available	1	5
Data Poisoning Attacks Against Federated Learning Systems	Jul 16, 2020	Data PoisoningFederated Learning	CodeCode Available	1	5
Data Valuation and Detections in Federated Learning	Nov 9, 2023	Data ValuationFederated Learning	CodeCode Available	1	5
An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportation	Aug 15, 2022	Federated Learning	CodeCode Available	1	5
An Efficient Approach for Cross-Silo Federated Learning to Rank	Jun 21, 2021	Federated LearningLearning-To-Rank	CodeCode Available	1	5
A New Federated Learning Framework Against Gradient Inversion Attacks	Dec 10, 2024	Federated LearningPrivacy Preserving	CodeCode Available	1	5

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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