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 426–450 of 6771 papers

Title	Date	Tasks	Status	Hype
Towards the Practical Utility of Federated Learning in the Medical Domain	Jul 7, 2022	Federated Learning	CodeCode Available	1
Federated Self-supervised Learning for Video Understanding	Jul 5, 2022	Action RecognitionFederated Learning	CodeCode Available	1
Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning	Jun 30, 2022	Federated Learning	CodeCode Available	1
FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image Classification	Jun 28, 2022	Contrastive LearningFederated Learning	CodeCode Available	1
An Empirical Study of Personalized Federated Learning	Jun 27, 2022	BIG-bench Machine LearningFederated Learning	CodeCode Available	1
Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance	Jun 27, 2022	Federated Learning	CodeCode Available	1
How to Combine Variational Bayesian Networks in Federated Learning	Jun 22, 2022	Federated Learningimage-classification	CodeCode Available	1
Personalized Subgraph Federated Learning	Jun 21, 2022	Federated Learning	CodeCode Available	1
Mitigating Data Heterogeneity in Federated Learning with Data Augmentation	Jun 20, 2022	Data AugmentationDomain Generalization	CodeCode Available	1
Shuffle Gaussian Mechanism for Differential Privacy	Jun 20, 2022	Federated Learning	CodeCode Available	1
Pisces: Efficient Federated Learning via Guided Asynchronous Training	Jun 18, 2022	Federated LearningNavigate	CodeCode Available	1
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning	Jun 17, 2022	Federated LearningPrivacy Preserving	CodeCode Available	1
FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification	Jun 17, 2022	Federated LearningFew-Shot Learning	CodeCode Available	1
On Privacy and Personalization in Cross-Silo Federated Learning	Jun 16, 2022	Federated LearningMulti-Task Learning	CodeCode Available	1
Personalized Federated Learning via Variational Bayesian Inference	Jun 16, 2022	Bayesian InferenceDiversity	CodeCode Available	1
Federated Multi-organ Segmentation with Inconsistent Labels	Jun 14, 2022	DecoderFederated Learning	CodeCode Available	1
Federated Optimization Algorithms with Random Reshuffling and Gradient Compression	Jun 14, 2022	Federated LearningQuantization	CodeCode Available	1
Neurotoxin: Durable Backdoors in Federated Learning	Jun 12, 2022	Backdoor AttackFederated Learning	CodeCode Available	1
Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT Imaging	Jun 8, 2022	Computed Tomography (CT)CT Reconstruction	CodeCode Available	1
Privacy Amplification via Shuffled Check-Ins	Jun 7, 2022	Federated Learning	CodeCode Available	1
Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning	Jun 6, 2022	Federated Learning	CodeCode Available	1
Pretrained Models for Multilingual Federated Learning	Jun 6, 2022	Federated LearningLanguage Modeling	CodeCode Available	1
Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification	Jun 3, 2022	Deep LearningFederated Learning	CodeCode Available	1
A federated graph neural network framework for privacy-preserving personalization	Jun 2, 2022	Federated LearningGraph Neural Network	CodeCode Available	1
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training	Jun 1, 2022	Federated LearningPersonalized Federated 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