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 6651–6675 of 6771 papers

Title	Date	Tasks	Status	Hype
BAFFLE : Blockchain Based Aggregator Free Federated Learning	Sep 16, 2019	Computational EfficiencyFederated Learning	CodeCode Available	0
Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification	Sep 13, 2019	Federated LearningGeneral Classification	CodeCode Available	1
Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction	Sep 12, 2019	Distributed OptimizationFederated Learning	CodeCode Available	1
Differentially Private Meta-Learning	Sep 12, 2019	Federated LearningFew-Shot Learning	—Unverified	0
Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging	Sep 11, 2019	BIG-bench Machine LearningFederated Learning	—Unverified	0
HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography	Sep 11, 2019	EEGElectroencephalogram (EEG)	CodeCode Available	0
First Analysis of Local GD on Heterogeneous Data	Sep 10, 2019	Federated Learning	—Unverified	0
Gradient Descent with Compressed Iterates	Sep 10, 2019	Federated Learning	—Unverified	0
Hierarchical Federated Learning Across Heterogeneous Cellular Networks	Sep 5, 2019	Federated Learning	—Unverified	0
Energy Demand Prediction with Federated Learning for Electric Vehicle Networks	Sep 3, 2019	BIG-bench Machine LearningClustering	—Unverified	0
Rewarding High-Quality Data via Influence Functions	Aug 30, 2019	Federated LearningVocal Bursts Intensity Prediction	—Unverified	0
An End-to-End Encrypted Neural Network for Gradient Updates Transmission in Federated Learning	Aug 22, 2019	Data CompressionFederated Learning	—Unverified	0
Decentralized Federated Learning: A Segmented Gossip Approach	Aug 21, 2019	Federated Learning	CodeCode Available	1
Federated Learning: Challenges, Methods, and Future Directions	Aug 21, 2019	BIG-bench Machine LearningDistributed Optimization	CodeCode Available	0
Towards Effective Device-Aware Federated Learning	Aug 20, 2019	Federated LearningInformation Retrieval	—Unverified	0
On Analog Gradient Descent Learning over Multiple Access Fading Channels	Aug 20, 2019	Federated Learning	CodeCode Available	1
Federated Learning with Additional Mechanisms on Clients to Reduce Communication Costs	Aug 16, 2019	Federated Learning	CodeCode Available	0
Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges	Jul 30, 2019	Federated LearningPrivacy Preserving	—Unverified	0
A Federated Learning Approach for Mobile Packet Classification	Jul 30, 2019	Classificationfeature selection	—Unverified	0
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection	Jul 23, 2019	BIG-bench Machine LearningFederated Learning	CodeCode Available	0
Federated Learning over Wireless Fading Channels	Jul 23, 2019	Federated Learning	—Unverified	0
FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare	Jul 22, 2019	Activity RecognitionFederated Learning	—Unverified	0
The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost	Jul 16, 2019	Anomaly DetectionFederated Learning	CodeCode Available	0
Privacy-Preserving Classification with Secret Vector Machines	Jul 8, 2019	ClassificationFederated Learning	CodeCode Available	0
Wireless Federated Distillation for Distributed Edge Learning with Heterogeneous Data	Jul 5, 2019	BIG-bench Machine LearningFederated 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