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 5201–5225 of 6771 papers

Title	Date	Tasks	Status	Hype
Privacy-preserving Federated Learning for Residential Short Term Load Forecasting	Nov 17, 2021	Federated LearningLoad Forecasting	—Unverified	0
Differentially Private Federated Learning on Heterogeneous Data	Nov 17, 2021	Federated Learning	CodeCode Available	1
A Parameter Aggregation Strategy on Personalized Federated Learning	Nov 16, 2021	Edge-computingFederated Learning	—Unverified	0
Learning Tokenization in Private Federated Learning with Sub-Word Model Sampling	Nov 16, 2021	Federated LearningLanguage Modeling	—Unverified	0
FedParsing: a Semi-Supervised Federated Learning Model on Semantic Parsing	Nov 16, 2021	Federated LearningSemantic Parsing	—Unverified	0
HADFL: Heterogeneity-aware Decentralized Federated Learning Framework	Nov 16, 2021	Federated Learning	—Unverified	0
FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning	Nov 16, 2021	Federated LearningPrivacy Preserving	CodeCode Available	0
Wyner-Ziv Gradient Compression for Federated Learning	Nov 16, 2021	Federated LearningQuantization	—Unverified	0
FedCostWAvg: A new averaging for better Federated Learning	Nov 16, 2021	Federated LearningSegmentation	—Unverified	0
Federated Learning for Smart Healthcare: A Survey	Nov 16, 2021	Federated LearningManagement	—Unverified	0
On-Demand Unlabeled Personalized Federated Learning	Nov 16, 2021	Domain AdaptationFederated Learning	—Unverified	0
DNN gradient lossless compression: Can GenNorm be the answer?	Nov 15, 2021	Federated Learning	CodeCode Available	0
Federated Learning for Internet of Things: Applications, Challenges, and Opportunities	Nov 15, 2021	Federated Learning	—Unverified	0
On the Tradeoff between Energy, Precision, and Accuracy in Federated Quantized Neural Networks	Nov 15, 2021	Federated LearningQuantization	—Unverified	0
Power Allocation for Wireless Federated Learning using Graph Neural Networks	Nov 15, 2021	Federated Learning	CodeCode Available	1
Federated Learning with Hyperparameter-based Clustering for Electrical Load Forecasting	Nov 14, 2021	ClusteringEdge-computing	—Unverified	0
Eluding Secure Aggregation in Federated Learning via Model Inconsistency	Nov 14, 2021	Federated Learning	CodeCode Available	1
Attentive Federated Learning for Concept Drift in Distributed 5G Edge Networks	Nov 14, 2021	Continual LearningEdge-computing	CodeCode Available	0
Towards Privacy-Preserving Affect Recognition: A Two-Level Deep Learning Architecture	Nov 14, 2021	Federated LearningPrivacy Preserving	—Unverified	0
Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges	Nov 14, 2021	Federated LearningSurvey	—Unverified	0
An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning	Nov 13, 2021	Anomaly DetectionFederated Learning	—Unverified	0
STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networks	Nov 12, 2021	Federated Learning	CodeCode Available	0
Hierarchical Bayesian Bandits	Nov 12, 2021	Federated LearningThompson Sampling	—Unverified	0
FedGreen: Federated Learning with Fine-Grained Gradient Compression for Green Mobile Edge Computing	Nov 11, 2021	Edge-computingFederated Learning	—Unverified	0
Fairness, Integrity, and Privacy in a Scalable Blockchain-based Federated Learning System	Nov 11, 2021	FairnessFederated 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