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 6451–6475 of 6771 papers

Title	Date	Tasks	Status
FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction	Jun 12, 2020	Federated Learning	—Unverified
Backdoor Attacks on Federated Meta-Learning	Jun 12, 2020	Federated LearningMeta-Learning	—Unverified
An Accurate, Scalable and Verifiable Protocol for Federated Differentially Private Averaging	Jun 12, 2020	Federated Learning	—Unverified
Understanding Unintended Memorization in Federated Learning	Jun 12, 2020	ClusteringFederated Learning	—Unverified
Towards Flexible Device Participation in Federated Learning	Jun 12, 2020	Federated Learning	—Unverified
Federated and continual learning for classification tasks in a society of devices	Jun 12, 2020	Continual LearningFederated Learning	—Unverified
Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data	Jun 12, 2020	FairnessFederated Learning	—Unverified
SECure: A Social and Environmental Certificate for AI Systems	Jun 11, 2020	BIG-bench Machine LearningFederated Learning	—Unverified
XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning	Jun 9, 2020	Data AugmentationFederated Learning	—Unverified
Distributed Learning on Heterogeneous Resource-Constrained Devices	Jun 9, 2020	Federated LearningReinforcement Learning (RL)	—Unverified
Attacks to Federated Learning: Responsive Web User Interface to Recover Training Data from User Gradients	Jun 8, 2020	Federated Learning	—Unverified
From Federated to Fog Learning: Distributed Machine Learning over Heterogeneous Wireless Networks	Jun 7, 2020	BIG-bench Machine LearningFederated Learning	—Unverified
LDP-Fed: Federated Learning with Local Differential Privacy	Jun 5, 2020	Federated Learning	—Unverified
Federated Learning for 6G Communications: Challenges, Methods, and Future Directions	Jun 4, 2020	Federated Learning	—Unverified
Wireless Communications for Collaborative Federated Learning	Jun 3, 2020	BIG-bench Machine LearningFederated Learning	—Unverified
Federated Learning in Vehicular Networks	Jun 2, 2020	Autonomous DrivingFederated Learning	—Unverified
Secure Sum Outperforms Homomorphic Encryption in (Current) Collaborative Deep Learning	Jun 2, 2020	Deep LearningFederated Learning	—Unverified
Federated Face Presentation Attack Detection	May 29, 2020	Face Anti-SpoofingFace Presentation Attack Detection	—Unverified
Towards Efficient Scheduling of Federated Mobile Devices under Computational and Statistical Heterogeneity	May 25, 2020	Federated LearningScheduling	—Unverified
MVStylizer: An Efficient Edge-Assisted Video Photorealistic Style Transfer System for Mobile Phones	May 24, 2020	Federated LearningOptical Flow Estimation	—Unverified
Reliability and Performance Assessment of Federated Learning on Clinical Benchmark Data	May 24, 2020	Federated Learning	—Unverified
Training Keyword Spotting Models on Non-IID Data with Federated Learning	May 21, 2020	Data AugmentationFederated Learning	—Unverified
Global Multiclass Classification and Dataset Construction via Heterogeneous Local Experts	May 21, 2020	ClassificationFederated Learning	—Unverified
Federated Learning for Hybrid Beamforming in mm-Wave Massive MIMO	May 20, 2020	BIG-bench Machine LearningFederated Learning	—Unverified
Large-Scale Secure XGB for Vertical Federated Learning	May 18, 2020	BIG-bench Machine LearningFederated Learning	—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