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 4451–4475 of 6771 papers

Title	Date	Tasks	Status	Hype
Multi-Level Branched Regularization for Federated Learning	Jul 14, 2022	Federated LearningKnowledge Distillation	CodeCode Available	1
Accelerated Federated Learning with Decoupled Adaptive Optimization	Jul 14, 2022	Federated Learning	—Unverified	0
One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesis	Jul 13, 2022	AllFederated Learning	CodeCode Available	1
Enhanced Security and Privacy via Fragmented Federated Learning	Jul 13, 2022	Federated Learning	CodeCode Available	0
Federated Multi-Task Learning for THz Wideband Channel and DoA Estimation	Jul 13, 2022	Federated LearningMulti-Task Learning	—Unverified	0
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels	Jul 13, 2022	Federated Learning	CodeCode Available	0
Efficient and Privacy Preserving Group Signature for Federated Learning	Jul 12, 2022	Federated LearningPrivacy Preserving	—Unverified	0
FedPseudo: Pseudo value-based Deep Learning Models for Federated Survival Analysis	Jul 12, 2022	Federated LearningSurvival Analysis	—Unverified	0
Federated Unlearning: How to Efficiently Erase a Client in FL?	Jul 12, 2022	Federated Learning	CodeCode Available	1
FedHAP: Federated Hashing with Global Prototypes for Cross-silo Retrieval	Jul 12, 2022	Deep HashingFederated Learning	—Unverified	0
Horizontal Federated Learning and Secure Distributed Training for Recommendation System with Intel SGX	Jul 11, 2022	Deep LearningFederated Learning	—Unverified	0
Personalizing Federated Medical Image Segmentation via Local Calibration	Jul 11, 2022	channel selectionFederated Learning	CodeCode Available	1
Mechanisms that Incentivize Data Sharing in Federated Learning	Jul 10, 2022	Federated Learning	—Unverified	0
FedSS: Federated Learning with Smart Selection of clients	Jul 10, 2022	Federated LearningScheduling	—Unverified	0
The Poisson binomial mechanism for secure and private federated learning	Jul 9, 2022	Federated Learning	—Unverified	0
Smart Multi-tenant Federated Learning	Jul 9, 2022	Federated Learning	—Unverified	0
Multi-Model Federated Learning with Provable Guarantees	Jul 9, 2022	Federated Learningmodel	—Unverified	0
Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning	Jul 9, 2022	Distributed OptimizationFederated Learning	CodeCode Available	0
StatMix: Data augmentation method that relies on image statistics in federated learning	Jul 8, 2022	Data AugmentationFederated Learning	—Unverified	0
Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with Inexact Prox	Jul 8, 2022	Federated Learning	—Unverified	0
A Survey on Participant Selection for Federated Learning in Mobile Networks	Jul 8, 2022	Federated LearningPrivacy Preserving	—Unverified	0
Decentralized digital twins of complex dynamical systems	Jul 7, 2022	BIG-bench Machine LearningFederated Learning	—Unverified	0
Towards the Practical Utility of Federated Learning in the Medical Domain	Jul 7, 2022	Federated Learning	CodeCode Available	1
Adaptive Personlization in Federated Learning for Highly Non-i.i.d. Data	Jul 7, 2022	ClusteringFederated Learning	—Unverified	0
FedHeN: Federated Learning in Heterogeneous Networks	Jul 7, 2022	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