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 4276–4300 of 6771 papers

Title	Date	Tasks	Status	Hype
Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated Learning	Sep 24, 2022	Federated LearningMeta-Learning	CodeCode Available	0
Communication-Efficient Federated Learning Using Censored Heavy Ball Descent	Sep 24, 2022	Federated Learning	—Unverified	0
Defending against Poisoning Backdoor Attacks on Federated Meta-learning	Sep 23, 2022	Federated LearningMeta-Learning	—Unverified	0
Privacy-Preserving Online Content Moderation: A Federated Learning Use Case	Sep 23, 2022	CPUFederated Learning	—Unverified	0
UAV-Assisted Hierarchical Aggregation for Over-the-Air Federated Learning	Sep 23, 2022	Federated Learning	—Unverified	0
Differentially private partitioned variational inference	Sep 23, 2022	Federated LearningPrivacy Preserving	CodeCode Available	0
Enhanced Decentralized Federated Learning based on Consensus in Connected Vehicles	Sep 22, 2022	Decision MakingFederated Learning	—Unverified	0
A One-shot Framework for Distributed Clustered Learning in Heterogeneous Environments	Sep 22, 2022	ClusteringFederated Learning	—Unverified	0
FedFOR: Stateless Heterogeneous Federated Learning with First-Order Regularization	Sep 21, 2022	Federated Learning	CodeCode Available	0
Federated Learning from Pre-Trained Models: A Contrastive Learning Approach	Sep 21, 2022	Contrastive LearningFederated Learning	—Unverified	0
Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks	Sep 21, 2022	Federated LearningModel-based Reinforcement Learning	—Unverified	0
Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data Subspaces	Sep 21, 2022	Federated Learning	CodeCode Available	1
FedToken: Tokenized Incentives for Data Contribution in Federated Learning	Sep 20, 2022	Federated Learning	—Unverified	0
Heterogeneous Federated Learning on a Graph	Sep 19, 2022	Federated Learningparameter estimation	—Unverified	0
Characterizing Internal Evasion Attacks in Federated Learning	Sep 17, 2022	Adversarial RobustnessFederated Learning	CodeCode Available	1
Contrastive Re-localization and History Distillation in Federated CMR Segmentation	Sep 16, 2022	Federated LearningImage Segmentation	—Unverified	0
A Secure Healthcare 5.0 System Based on Blockchain Technology Entangled with Federated Learning Technique	Sep 16, 2022	Federated LearningIntrusion Detection	—Unverified	0
Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture Model using Subspace Perturbation	Sep 16, 2022	Federated LearningPrivacy Preserving	—Unverified	0
The Cost of Training Machine Learning Models over Distributed Data Sources	Sep 15, 2022	Federated Learning	CodeCode Available	0
Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approach	Sep 14, 2022	Federated Learning	—Unverified	0
Federated Pruning: Improving Neural Network Efficiency with Federated Learning	Sep 14, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Scheduling Algorithms for Federated Learning with Minimal Energy Consumption	Sep 13, 2022	Federated LearningMultiple-choice	—Unverified	0
Investigating the Predictive Reproducibility of Federated Graph Neural Networks using Medical Datasets	Sep 13, 2022	Federated Learning	CodeCode Available	0
Concealing Sensitive Samples against Gradient Leakage in Federated Learning	Sep 13, 2022	Federated LearningStochastic Optimization	CodeCode Available	0
Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer Learning	Sep 12, 2022	Federated Learningimage-classification	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