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 6376–6400 of 6771 papers

Title	Date	Tasks	Status	Hype
A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning	Jun 19, 2020	Federated LearningStochastic Optimization	CodeCode Available	1
FedFMC: Sequential Efficient Federated Learning on Non-iid Data	Jun 19, 2020	Federated Learning	—Unverified	0
Federated Learning With Quantized Global Model Updates	Jun 18, 2020	Federated Learningmodel	—Unverified	0
FedCD: Improving Performance in non-IID Federated Learning	Jun 17, 2020	Federated Learning	CodeCode Available	1
Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixup	Jun 17, 2020	Federated LearningPrivacy Preserving	—Unverified	0
Communication-Efficient Robust Federated Learning Over Heterogeneous Datasets	Jun 17, 2020	Federated Learning	—Unverified	0
Federated Survival Analysis with Discrete-Time Cox Models	Jun 16, 2020	Federated LearningPrivacy Preserving	—Unverified	0
Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing	Jun 16, 2020	Distributed OptimizationFederated Learning	CodeCode Available	1
Robust Federated Learning: The Case of Affine Distribution Shifts	Jun 16, 2020	Federated Learningimage-classification	—Unverified	0
Personalized Federated Learning with Moreau Envelopes	Jun 16, 2020	DiversityFederated Learning	CodeCode Available	1
Fusion Learning: A One Shot Federated Learning	Jun 15, 2020	Federated Learning	—Unverified	0
The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems	Jun 14, 2020	Federated Learning	CodeCode Available	1
Understanding Unintended Memorization in Federated Learning	Jun 12, 2020	ClusteringFederated Learning	—Unverified	0
An Accurate, Scalable and Verifiable Protocol for Federated Differentially Private Averaging	Jun 12, 2020	Federated Learning	—Unverified	0
Towards Flexible Device Participation in Federated Learning	Jun 12, 2020	Federated Learning	—Unverified	0
Federated and continual learning for classification tasks in a society of devices	Jun 12, 2020	Continual LearningFederated Learning	—Unverified	0
FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction	Jun 12, 2020	Federated Learning	—Unverified	0
Ensemble Distillation for Robust Model Fusion in Federated Learning	Jun 12, 2020	BIG-bench Machine LearningFederated Learning	CodeCode Available	0
Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data	Jun 12, 2020	FairnessFederated Learning	—Unverified	0
Backdoor Attacks on Federated Meta-Learning	Jun 12, 2020	Federated LearningMeta-Learning	—Unverified	0
SECure: A Social and Environmental Certificate for AI Systems	Jun 11, 2020	BIG-bench Machine LearningFederated Learning	—Unverified	0
XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning	Jun 9, 2020	Data AugmentationFederated Learning	—Unverified	0
Distributed Learning on Heterogeneous Resource-Constrained Devices	Jun 9, 2020	Federated LearningReinforcement Learning (RL)	—Unverified	0
Locally Private Graph Neural Networks	Jun 9, 2020	Federated LearningNode Classification	CodeCode Available	1
ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing	Jun 8, 2020	CPUFederated Learning	CodeCode Available	1

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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