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 4501–4525 of 6771 papers

Title	Date	Tasks	Status	Hype
Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance	Jun 27, 2022	Federated Learning	CodeCode Available	1
An Empirical Study of Personalized Federated Learning	Jun 27, 2022	BIG-bench Machine LearningFederated Learning	CodeCode Available	1
Differentially Private Federated Combinatorial Bandits with Constraints	Jun 27, 2022	Federated LearningPrivacy Preserving	—Unverified	0
APPFLChain: A Privacy Protection Distributed Artificial-Intelligence Architecture Based on Federated Learning and Consortium Blockchain	Jun 26, 2022	Decision MakingFederated Learning	—Unverified	0
Cross-Silo Federated Learning: Challenges and Opportunities	Jun 26, 2022	Federated Learning	—Unverified	0
FAIR-BFL: Flexible and Incentive Redesign for Blockchain-based Federated Learning	Jun 26, 2022	Federated LearningMalicious Detection	—Unverified	0
FLVoogd: Robust And Privacy Preserving Federated Learning	Jun 24, 2022	Federated Learningimage-classification	—Unverified	0
zPROBE: Zero Peek Robustness Checks for Federated Learning	Jun 24, 2022	Federated LearningPrivacy Preserving	—Unverified	0
"You Can't Fix What You Can't Measure": Privately Measuring Demographic Performance Disparities in Federated Learning	Jun 24, 2022	Federated Learning	CodeCode Available	0
Using Autoencoders on Differentially Private Federated Learning GANs	Jun 24, 2022	AvgDenoising	CodeCode Available	0
FEATHERS: Federated Architecture and Hyperparameter Search	Jun 24, 2022	AutoMLBIG-bench Machine Learning	—Unverified	0
MULTI-FLGANs: Multi-Distributed Adversarial Networks for Non-IID distribution	Jun 24, 2022	Federated Learning	—Unverified	0
Data Leakage in Federated Averaging	Jun 24, 2022	Federated Learning	CodeCode Available	0
Content Popularity Prediction Based on Quantized Federated Bayesian Learning in Fog Radio Access Networks	Jun 23, 2022	Federated Learning	—Unverified	0
EFFGAN: Ensembles of fine-tuned federated GANs	Jun 23, 2022	Federated Learning	—Unverified	0
Efficient Adaptive Federated Optimization of Federated Learning for IoT	Jun 23, 2022	Federated LearningPrivacy Preserving	—Unverified	0
On the Importance and Applicability of Pre-Training for Federated Learning	Jun 23, 2022	Federated Learning	CodeCode Available	0
Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-term Utility Demand Forecasting in Electricity Wholesale Markets	Jun 23, 2022	Deep Reinforcement LearningDemand Forecasting	—Unverified	0
Quantization Robust Federated Learning for Efficient Inference on Heterogeneous Devices	Jun 22, 2022	BIG-bench Machine LearningFederated Learning	—Unverified	0
Federated Latent Class Regression for Hierarchical Data	Jun 22, 2022	Federated Learningregression	—Unverified	0
Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks	Jun 22, 2022	Bilevel OptimizationFederated Learning	—Unverified	0
FedorAS: Federated Architecture Search under system heterogeneity	Jun 22, 2022	Federated LearningNeural Architecture Search	—Unverified	0
FedBC: Calibrating Global and Local Models via Federated Learning Beyond Consensus	Jun 22, 2022	Federated Learning	—Unverified	0
How to Combine Variational Bayesian Networks in Federated Learning	Jun 22, 2022	Federated Learningimage-classification	CodeCode Available	1
An Efficient Industrial Federated Learning Framework for AIoT: A Face Recognition Application	Jun 21, 2022	Face RecognitionFederated 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