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 2676–2700 of 6771 papers

Title	Date	Tasks	Status	Hype
Scheduling and Communication Schemes for Decentralized Federated Learning	Nov 27, 2023	Federated LearningScheduling	—Unverified	0
A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspective	Nov 27, 2023	Federated LearningPrivacy Preserving	CodeCode Available	1
Using Decentralized Aggregation for Federated Learning with Differential Privacy	Nov 27, 2023	Federated Learning	—Unverified	0
Where to Begin? From Random to Foundation Model Instructed Initialization in Federated Learning for Medical Image Segmentation	Nov 27, 2023	Federated LearningImage Segmentation	—Unverified	0
QuickDrop: Efficient Federated Unlearning by Integrated Dataset Distillation	Nov 27, 2023	Dataset DistillationFederated Learning	—Unverified	0
Evaluating Multi-Global Server Architecture for Federated Learning	Nov 26, 2023	Federated Learning	—Unverified	0
Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs	Nov 26, 2023	Federated Learning	—Unverified	0
Eliminating Domain Bias for Federated Learning in Representation Space	Nov 25, 2023	Federated LearningPrivacy Preserving	CodeCode Available	4
OFDMA-F^2L: Federated Learning With Flexible Aggregation Over an OFDMA Air Interface	Nov 25, 2023	Federated Learning	—Unverified	0
Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networks	Nov 24, 2023	Anomaly DetectionFault Detection	—Unverified	0
FAMAC: A Federated Assisted Modified Actor-Critic Framework for Secured Energy Saving in 5G and Beyond Networks	Nov 24, 2023	Federated Learning	—Unverified	0
Prototype of deployment of Federated Learning with IoT devices	Nov 24, 2023	Federated Learning	—Unverified	0
ARIA: On the Interaction Between Architectures, Initialization and Aggregation Methods for Federated Visual Classification	Nov 24, 2023	Federated Learningimage-classification	CodeCode Available	0
Federated Learning for Short Text Clustering	Nov 23, 2023	ClusteringFederated Learning	—Unverified	0
A Blockchain Solution for Collaborative Machine Learning over IoT	Nov 23, 2023	Federated LearningIncremental Learning	—Unverified	0
Federated Learning Assisted Distributed Energy Optimization	Nov 23, 2023	Decision MakingDistributed Optimization	—Unverified	0
AdapterFL: Adaptive Heterogeneous Federated Learning for Resource-constrained Mobile Computing Systems	Nov 23, 2023	Federated Learning	—Unverified	0
OASIS: Offsetting Active Reconstruction Attacks in Federated Learning	Nov 23, 2023	Federated LearningImage Augmentation	—Unverified	0
Enhancing Intrusion Detection In Internet Of Vehicles Through Federated Learning	Nov 23, 2023	Federated LearningIntrusion Detection	—Unverified	0
Brain MRI Screening Tool with Federated Learning	Nov 23, 2023	Federated Learning	—Unverified	0
A Joint Gradient and Loss Based Clustered Federated Learning Design	Nov 22, 2023	ClusteringFederated Learning	—Unverified	0
FedHCA^2: Towards Hetero-Client Federated Multi-Task Learning	Nov 22, 2023	DecoderFederated Learning	CodeCode Available	1
FedFN: Feature Normalization for Alleviating Data Heterogeneity Problem in Federated Learning	Nov 22, 2023	Federated Learning	—Unverified	0
AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems	Nov 22, 2023	Federated Learning	—Unverified	0
SecureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning	Nov 22, 2023	Federated LearningMachine Unlearning	—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