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 3801–3825 of 6771 papers

Title	Date	Tasks	Status	Hype
Communication and Storage Efficient Federated Split Learning	Feb 11, 2023	Federated Learning	—Unverified	0
Enhance Local Consistency in Federated Learning: A Multi-Step Inertial Momentum Approach	Feb 11, 2023	Edge-computingFederated Learning	—Unverified	0
Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networks	Feb 11, 2023	DiagnosticFederated Learning	—Unverified	0
Privacy Against Agnostic Inference Attacks in Vertical Federated Learning	Feb 10, 2023	Federated LearningInference Attack	—Unverified	0
XFL: A High Performace, Lightweighted Federated Learning Framework	Feb 10, 2023	Federated LearningVocal Bursts Intensity Prediction	CodeCode Available	1
Achieving Linear Speedup in Non-IID Federated Bilevel Learning	Feb 10, 2023	Bilevel OptimizationFederated Learning	—Unverified	0
Principled Federated Domain Adaptation: Gradient Projection and Auto-Weighting	Feb 10, 2023	Domain AdaptationFederated Learning	CodeCode Available	0
Towards Fairer and More Efficient Federated Learning via Multidimensional Personalized Edge Models	Feb 9, 2023	Computational EfficiencyFairness	—Unverified	0
Delay Sensitive Hierarchical Federated Learning with Stochastic Local Updates	Feb 9, 2023	Federated Learning	—Unverified	0
Mithridates: Auditing and Boosting Backdoor Resistance of Machine Learning Pipelines	Feb 9, 2023	AutoMLFederated Learning	CodeCode Available	0
Near-Field Terahertz Communications: Model-Based and Model-Free Channel Estimation	Feb 9, 2023	Federated Learningmodel	—Unverified	0
Towards Model-Agnostic Federated Learning over Networks	Feb 8, 2023	Federated Learningmodel	CodeCode Available	0
Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach	Feb 8, 2023	Distributed OptimizationFederated Learning	CodeCode Available	1
Exploratory Analysis of Federated Learning Methods with Differential Privacy on MIMIC-III	Feb 8, 2023	DiagnosticFederated Learning	—Unverified	0
Improving the Model Consistency of Decentralized Federated Learning	Feb 8, 2023	Federated Learningmodel	—Unverified	0
Federated Variational Inference Methods for Structured Latent Variable Models	Feb 7, 2023	Federated LearningTopic Models	—Unverified	0
A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection	Feb 7, 2023	Federated LearningPrivacy Preserving	CodeCode Available	1
Federated Learning with Regularized Client Participation	Feb 7, 2023	Federated Learning	—Unverified	0
z-SignFedAvg: A Unified Stochastic Sign-based Compression for Federated Learning	Feb 6, 2023	Federated LearningPrivacy Preserving	—Unverified	0
Adaptive Parameterization of Deep Learning Models for Federated Learning	Feb 6, 2023	Deep LearningFederated Learning	—Unverified	0
Federated Survival Forests	Feb 6, 2023	Federated LearningMissing Values	CodeCode Available	0
One-shot Empirical Privacy Estimation for Federated Learning	Feb 6, 2023	Federated Learning	—Unverified	0
On the Convergence of Federated Averaging with Cyclic Client Participation	Feb 6, 2023	Federated Learning	—Unverified	0
Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey	Feb 6, 2023	Edge-computingFederated Learning	—Unverified	0
Cross-Fusion Rule for Personalized Federated Learning	Feb 6, 2023	Federated LearningPersonalized Federated Learning	—Unverified	0

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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