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 5876–5900 of 6771 papers

Title	Date	Tasks	Status	Hype
Optimization of User Selection and Bandwidth Allocation for Federated Learning in VLC/RF Systems	Mar 5, 2021	Federated Learning	—Unverified	0
FedDR -- Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization	Mar 5, 2021	Federated Learning	CodeCode Available	1
Semi-Supervised Federated Peer Learning for Skin Lesion Classification	Mar 5, 2021	Cancer ClassificationClassification	CodeCode Available	0
FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data	Mar 5, 2021	Federated LearningPrivacy Preserving	—Unverified	0
FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation	Mar 5, 2021	AnatomyAnomaly Detection	—Unverified	0
Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices	Mar 4, 2021	Distributed OptimizationFederated Learning	CodeCode Available	1
One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning	Mar 4, 2021	AllFederated Learning	CodeCode Available	0
Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning	Mar 3, 2021	Federated LearningImage Reconstruction	CodeCode Available	1
Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of Things	Mar 3, 2021	BIG-bench Machine LearningFederated Learning	CodeCode Available	1
Adaptive Transmission Scheduling in Wireless Networks for Asynchronous Federated Learning	Mar 2, 2021	Federated LearningScheduling	—Unverified	0
PFA: Privacy-preserving Federated Adaptation for Effective Model Personalization	Mar 2, 2021	Federated LearningPrivacy Preserving	CodeCode Available	1
ZeroSARAH: Efficient Nonconvex Finite-Sum Optimization with Zero Full Gradient Computation	Mar 2, 2021	Federated Learning	—Unverified	0
Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregation	Mar 2, 2021	Federated Learning	CodeCode Available	0
A Theorem of the Alternative for Personalized Federated Learning	Mar 2, 2021	Federated LearningPersonalized Federated Learning	—Unverified	0
Adversarial training in communication constrained federated learning	Mar 1, 2021	AttributeFederated Learning	—Unverified	0
FedPower: Privacy-Preserving Distributed Eigenspace Estimation	Mar 1, 2021	BIG-bench Machine LearningDimensionality Reduction	—Unverified	0
Towards Personalized Federated Learning	Mar 1, 2021	BenchmarkingFederated Learning	—Unverified	0
Heterogeneity for the Win: One-Shot Federated Clustering	Mar 1, 2021	ClusteringFederated Learning	CodeCode Available	1
Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating	Mar 1, 2021	Federated LearningVertical Federated Learning	—Unverified	0
Blockchain-Based Federated Learning in Mobile Edge Networks with Application in Internet of Vehicles	Mar 1, 2021	Edge-computingFederated Learning	—Unverified	0
Federated Learning without Revealing the Decision Boundaries	Mar 1, 2021	Federated LearningPrivacy Preserving	—Unverified	0
Scalable federated machine learning with FEDn	Feb 27, 2021	BIG-bench Machine LearningFederated Learning	CodeCode Available	1
Constrained Differentially Private Federated Learning for Low-bandwidth Devices	Feb 27, 2021	Federated LearningPrivacy Preserving	—Unverified	0
Efficient Client Contribution Evaluation for Horizontal Federated Learning	Feb 26, 2021	Federated Learning	—Unverified	0
Practical and Private (Deep) Learning without Sampling or Shuffling	Feb 26, 2021	Deep LearningFederated 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