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 2851–2875 of 6771 papers

Title	Date	Tasks	Status
Empowering Data Mesh with Federated Learning	Mar 26, 2024	Decision MakingFederated Learning	CodeCode Available
Not All Federated Learning Algorithms Are Created Equal: A Performance Evaluation Study	Mar 26, 2024	AllFederated Learning	—Unverified
Secure Aggregation is Not Private Against Membership Inference Attacks	Mar 26, 2024	Federated LearningPrivacy Preserving	CodeCode Available
GPFL: A Gradient Projection-Based Client Selection Framework for Efficient Federated Learning	Mar 26, 2024	Federated Learning	—Unverified
Leak and Learn: An Attacker's Cookbook to Train Using Leaked Data from Federated Learning	Mar 26, 2024	Federated Learning	—Unverified
Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data	Mar 25, 2024	Federated LearningFederated Unsupervised Learning	—Unverified
Distributed collaborative anomalous sound detection by embedding sharing	Mar 25, 2024	Federated Learning	—Unverified
Differentially Private Online Federated Learning with Correlated Noise	Mar 25, 2024	Federated Learning	—Unverified
FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning	Mar 25, 2024	Federated Learning	—Unverified
Accelerating Federated Learning by Selecting Beneficial Herd of Local Gradients	Mar 25, 2024	Federated Learning	—Unverified
FLIGAN: Enhancing Federated Learning with Incomplete Data using GAN	Mar 25, 2024	Federated LearningPrivacy Preserving	—Unverified
FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data	Mar 25, 2024	Dimensionality ReductionFederated Learning	—Unverified
Heterogeneous Federated Learning with Splited Language Model	Mar 24, 2024	Federated LearningLanguage Modeling	—Unverified
A Federated Parameter Aggregation Method for Node Classification Tasks with Different Graph Network Structures	Mar 24, 2024	Federated LearningGraph Neural Network	—Unverified
Initialisation and Network Effects in Decentralised Federated Learning	Mar 23, 2024	Federated Learning	—Unverified
TablePuppet: A Generic Framework for Relational Federated Learning	Mar 23, 2024	Federated Learning	CodeCode Available
Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization	Mar 22, 2024	Domain GeneralizationFederated Learning	—Unverified
Adaptive Coded Federated Learning: Privacy Preservation and Straggler Mitigation	Mar 22, 2024	Federated Learning	—Unverified
Federated Bayesian Deep Learning: The Application of Statistical Aggregation Methods to Bayesian Models	Mar 22, 2024	Federated LearningUncertainty Quantification	—Unverified
Loop Improvement: An Efficient Approach for Extracting Shared Features from Heterogeneous Data without Central Server	Mar 21, 2024	Federated LearningMulti-Task Learning	CodeCode Available
FedMef: Towards Memory-efficient Federated Dynamic Pruning	Mar 21, 2024	Federated LearningNetwork Pruning	—Unverified
Advancing IIoT with Over-the-Air Federated Learning: The Role of Iterative Magnitude Pruning	Mar 21, 2024	Federated LearningModel Compression	—Unverified
When Cars meet Drones: Hyperbolic Federated Learning for Source-Free Domain Adaptation in Adverse Weather	Mar 20, 2024	Autonomous VehiclesDomain Adaptation	CodeCode Available
Byzantine-resilient Federated Learning With Adaptivity to Data Heterogeneity	Mar 20, 2024	Federated Learning	—Unverified
Fed-RAC: Resource-Aware Clustering for Tackling Heterogeneity of Participants in Federated Learning	Mar 20, 2024	ClusteringFederated Learning	CodeCode Available

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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