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 4176–4200 of 6771 papers

Title	Date	Tasks	Status
Report of the Medical Image De-Identification (MIDI) Task Group -- Best Practices and Recommendations	Mar 18, 2023	De-identificationFederated Learning	—Unverified
An Empirical Evaluation of Federated Contextual Bandit Algorithms	Mar 17, 2023	Federated LearningMulti-Armed Bandits	—Unverified
GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Data	Mar 16, 2023	Federated LearningGraph Neural Network	CodeCode Available
Efficient and Secure Federated Learning for Financial Applications	Mar 15, 2023	Federated Learning	—Unverified
Comparative Evaluation of Data Decoupling Techniques for Federated Machine Learning with Database as a Service	Mar 15, 2023	Federated LearningManagement	—Unverified
Optimization Design for Federated Learning in Heterogeneous 6G Networks	Mar 15, 2023	Federated LearningManagement	—Unverified
Towards Cooperative Federated Learning over Heterogeneous Edge/Fog Networks	Mar 15, 2023	Federated Learning	—Unverified
Visual Prompt Based Personalized Federated Learning	Mar 15, 2023	Federated Learningimage-classification	—Unverified
Model Extraction Attacks on Split Federated Learning	Mar 13, 2023	BlockingFederated Learning	—Unverified
Network Anomaly Detection Using Federated Learning	Mar 13, 2023	Anomaly DetectionFederated Learning	—Unverified
Cross-device Federated Learning for Mobile Health Diagnostics: A First Study on COVID-19 Detection	Mar 13, 2023	DiagnosticFederated Learning	—Unverified
Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging	Mar 12, 2023	Brain Tumor SegmentationFederated Learning	—Unverified
Making Batch Normalization Great in Federated Deep Learning	Mar 12, 2023	Deep LearningFederated Learning	—Unverified
Stabilizing and Improving Federated Learning with Non-IID Data and Client Dropout	Mar 11, 2023	Federated Learning	—Unverified
Privacy-Preserving Cooperative Visible Light Positioning for Nonstationary Environment: A Federated Learning Perspective	Mar 11, 2023	Federated LearningPrivacy Preserving	—Unverified
Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learning	Mar 10, 2023	Federated LearningKnowledge Distillation	—Unverified
Optimizing Federated Learning for Medical Image Classification on Distributed Non-iid Datasets with Partial Labels	Mar 10, 2023	Federated Learningimage-classification	—Unverified
Papaya: Federated Learning, but Fully Decentralized	Mar 10, 2023	Federated Learning	—Unverified
Complement Sparsification: Low-Overhead Model Pruning for Federated Learning	Mar 10, 2023	Federated LearningPrivacy Preserving	—Unverified
Zone-based Federated Learning for Mobile Sensing Data	Mar 10, 2023	Activity RecognitionFederated Learning	—Unverified
FedREP: A Byzantine-Robust, Communication-Efficient and Privacy-Preserving Framework for Federated Learning	Mar 9, 2023	Federated LearningPrivacy Preserving	—Unverified
An Evaluation of Non-Contrastive Self-Supervised Learning for Federated Medical Image Analysis	Mar 9, 2023	Contrastive LearningFederated Learning	—Unverified
Memory-adaptive Depth-wise Heterogenous Federated Learning	Mar 8, 2023	Federated Learning	CodeCode Available
Model-Agnostic Federated Learning	Mar 8, 2023	Federated Learningmodel	CodeCode Available
Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clustering	Mar 8, 2023	Bayesian InferenceClustering	—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