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 6326–6350 of 6771 papers

Title	Date	Tasks	Status	Hype
Less is More: A privacy-respecting Android malware classifier using Federated Learning	Jul 16, 2020	Federated LearningMalware Classification	CodeCode Available	0
HyperTune: Dynamic Hyperparameter Tuning For Efficient Distribution of DNN Training Over Heterogeneous Systems	Jul 16, 2020	Federated Learningimage-classification	—Unverified	0
Federated Learning in Mobile Edge Computing: An Edge-Learning Perspective for Beyond 5G	Jul 15, 2020	BIG-bench Machine LearningEdge-computing	—Unverified	0
FetchSGD: Communication-Efficient Federated Learning with Sketching	Jul 15, 2020	Federated Learning	—Unverified	0
FedBoosting: Federated Learning with Gradient Protected Boosting for Text Recognition	Jul 14, 2020	Federated Learning	CodeCode Available	0
Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning	Jul 14, 2020	Federated LearningScheduling	—Unverified	0
Data-driven geophysics: from dictionary learning to deep learning	Jul 13, 2020	Deep LearningDictionary Learning	—Unverified	0
Model Fusion with Kullback--Leibler Divergence	Jul 13, 2020	Federated Learningmodel	CodeCode Available	0
Quality Inference in Federated Learning with Secure Aggregation	Jul 13, 2020	Federated LearningInference Attack	—Unverified	0
Privacy Amplification via Random Check-Ins	Jul 13, 2020	Federated Learning	—Unverified	0
VAFL: a Method of Vertical Asynchronous Federated Learning	Jul 12, 2020	Federated Learning	—Unverified	0
A Unified Linear Speedup Analysis of Federated Averaging and Nesterov FedAvg	Jul 11, 2020	Distributed OptimizationFederated Learning	—Unverified	0
Data science and AI in FinTech: An overview	Jul 10, 2020	BIG-bench Machine LearningFederated Learning	—Unverified	0
Differentially private cross-silo federated learning	Jul 10, 2020	Federated Learning	CodeCode Available	1
Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imaging	Jul 10, 2020	Computed Tomography (CT)Federated Learning	CodeCode Available	1
Client Adaptation improves Federated Learning with Simulated Non-IID Clients	Jul 9, 2020	Federated Learning	CodeCode Available	0
Federated Learning of User Authentication Models	Jul 9, 2020	Federated LearningPrivacy Preserving	—Unverified	0
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning	Jul 9, 2020	FairnessFederated Learning	CodeCode Available	1
BlockFLow: An Accountable and Privacy-Preserving Solution for Federated Learning	Jul 8, 2020	Federated LearningPrivacy Preserving	—Unverified	0
Self-organizing Democratized Learning: Towards Large-scale Distributed Learning Systems	Jul 7, 2020	ClusteringFederated Learning	CodeCode Available	0
Personalized Cross-Silo Federated Learning on Non-IID Data	Jul 7, 2020	Federated Learning	—Unverified	0
A Federated F-score Based Ensemble Model for Automatic Rule Extraction	Jul 7, 2020	Federated LearningMarketing	—Unverified	0
Coded Computing for Federated Learning at the Edge	Jul 7, 2020	Edge-computingFederated Learning	—Unverified	0
Learning while Respecting Privacy and Robustness to Distributional Uncertainties and Adversarial Data	Jul 7, 2020	Federated Learning	—Unverified	0
A Distributed Cubic-Regularized Newton Method for Smooth Convex Optimization over Networks	Jul 7, 2020	Federated 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