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 351–375 of 6771 papers

Title	Date	Tasks	Status	Hype
Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks	Jan 17, 2023	Federated Learning	CodeCode Available	1
Federated Learning under Heterogeneous and Correlated Client Availability	Jan 11, 2023	Federated Learning	CodeCode Available	1
Efficient On-device Training via Gradient Filtering	Jan 1, 2023	Federated Learningimage-classification	CodeCode Available	1
ScaleFL: Resource-Adaptive Federated Learning With Heterogeneous Clients	Jan 1, 2023	Federated LearningSST-2	CodeCode Available	1
Rethinking Federated Learning With Domain Shift: A Prototype View	Jan 1, 2023	Federated LearningPrivacy Preserving	CodeCode Available	1
When Federated Learning Meets Pre-trained Language Models' Parameter-Efficient Tuning Methods	Dec 20, 2022	Federated Learning	CodeCode Available	1
FedTADBench: Federated Time-Series Anomaly Detection Benchmark	Dec 19, 2022	Anomaly DetectionFederated Learning	CodeCode Available	1
Modeling Global Distribution for Federated Learning with Label Distribution Skew	Dec 17, 2022	Federated LearningGenerative Adversarial Network	CodeCode Available	1
Tackling Data Heterogeneity in Federated Learning with Class Prototypes	Dec 6, 2022	Federated LearningPersonalized Federated Learning	CodeCode Available	1
On the effectiveness of partial variance reduction in federated learning with heterogeneous data	Dec 5, 2022	DiversityFederated Learning	CodeCode Available	1
FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction	Dec 3, 2022	Federated Learningmodel	CodeCode Available	1
FedALA: Adaptive Local Aggregation for Personalized Federated Learning	Dec 2, 2022	Federated LearningPersonalized Federated Learning	CodeCode Available	1
Federated Learning for 5G Base Station Traffic Forecasting	Nov 28, 2022	Federated LearningManagement	CodeCode Available	1
FedGS: Federated Graph-based Sampling with Arbitrary Client Availability	Nov 25, 2022	Federated Learning	CodeCode Available	1
Knowledge-Aware Federated Active Learning with Non-IID Data	Nov 24, 2022	Active LearningFederated Learning	CodeCode Available	1
Federated Learning on Non-IID Graphs via Structural Knowledge Sharing	Nov 23, 2022	Federated LearningGraph Learning	CodeCode Available	1
A Dynamic Weighted Federated Learning for Android Malware Classification	Nov 23, 2022	ClassificationFederated Learning	CodeCode Available	1
Fed-TDA: Federated Tabular Data Augmentation on Non-IID Data	Nov 22, 2022	Data AugmentationFederated Learning	CodeCode Available	1
DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics	Nov 20, 2022	Federated Learning	CodeCode Available	1
FedDCT: Federated Learning of Large Convolutional Neural Networks on Resource Constrained Devices using Divide and Collaborative Training	Nov 20, 2022	Federated Learning	CodeCode Available	1
FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous Data	Nov 17, 2022	Classifier calibrationFederated Learning	CodeCode Available	1
Federated Adaptive Prompt Tuning for Multi-Domain Collaborative Learning	Nov 15, 2022	Federated Learningimage-classification	CodeCode Available	1
Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges	Nov 15, 2022	Federated Learning	CodeCode Available	1
FedCL: Federated Multi-Phase Curriculum Learning to Synchronously Correlate User Heterogeneity	Nov 14, 2022	Federated LearningKnowledge Distillation	CodeCode Available	1
Universal EHR Federated Learning Framework	Nov 14, 2022	Federated Learning	CodeCode Available	1

Show:10 25 50

← PrevPage 15 of 271Next →

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