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 5051–5075 of 6771 papers

Title	Date	Tasks	Status	Hype
Variance-Reduced Heterogeneous Federated Learning via Stratified Client Selection	Jan 15, 2022	DiversityFederated Learning	—Unverified	0
Demystifying Swarm Learning: A New Paradigm of Blockchain-based Decentralized Federated Learning	Jan 14, 2022	Federated LearningPrivacy Preserving	—Unverified	0
Federated Continual Learning for Socially Aware Robotics	Jan 14, 2022	Continual LearningFederated Learning	—Unverified	0
Jamming Attacks on Federated Learning in Wireless Networks	Jan 13, 2022	Federated Learning	—Unverified	0
Partial Model Averaging in Federated Learning: Performance Guarantees and Benefits	Jan 11, 2022	Federated Learning	—Unverified	0
An Interpretable Federated Learning-based Network Intrusion Detection Framework	Jan 10, 2022	Federated LearningIntrusion Detection	—Unverified	0
FedDTG:Federated Data-Free Knowledge Distillation via Three-Player Generative Adversarial Networks	Jan 10, 2022	Data-free Knowledge DistillationFederated Learning	—Unverified	0
Communication-Efficient Federated Learning with Accelerated Client Gradient	Jan 10, 2022	Federated Learning	CodeCode Available	1
A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning	Jan 9, 2022	Federated LearningMulti-agent Reinforcement Learning	—Unverified	0
A Fair and Efficient Hybrid Federated Learning Framework based on XGBoost for Distributed Power Prediction	Jan 8, 2022	FairnessFederated Learning	—Unverified	0
LoMar: A Local Defense Against Poisoning Attack on Federated Learning	Jan 8, 2022	Density EstimationEdge-computing	—Unverified	0
ADI: Adversarial Dominating Inputs in Vertical Federated Learning Systems	Jan 8, 2022	Federated LearningPrivacy Preserving	—Unverified	0
Fair and efficient contribution valuation for vertical federated learning	Jan 7, 2022	FairnessFederated Learning	—Unverified	0
Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion Theory	Jan 7, 2022	Federated LearningQuantization	CodeCode Available	0
Auction-Based Ex-Post-Payment Incentive Mechanism Design for Horizontal Federated Learning with Reputation and Contribution Measurement	Jan 7, 2022	Computational EfficiencyFederated Learning	—Unverified	0
Multi-Model Federated Learning	Jan 7, 2022	Federated Learningmodel	—Unverified	0
Federated Optimization of Smooth Loss Functions	Jan 6, 2022	Federated Learning	—Unverified	0
Towards Understanding Quality Challenges of the Federated Learning for Neural Networks: A First Look from the Lens of Robustness	Jan 5, 2022	Data PoisoningFederated Learning	CodeCode Available	0
FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clients	Jan 5, 2022	Federated Learning	CodeCode Available	1
Semantics-Preserved Distortion for Personal Privacy Protection in Information Management	Jan 4, 2022	AttributeConstituency Parsing	—Unverified	0
Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Drones	Jan 3, 2022	Federated LearningPrivacy Preserving	—Unverified	0
DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection	Jan 3, 2022	Federated Learning	—Unverified	0
Learning To Collaborate in Decentralized Learning of Personalized Models	Jan 1, 2022	Federated Learningimage-classification	—Unverified	0
Robust Federated Learning With Noisy and Heterogeneous Clients	Jan 1, 2022	Federated Learning	CodeCode Available	1
Learn From Others and Be Yourself in Heterogeneous Federated Learning	Jan 1, 2022	Continual LearningFederated Learning	CodeCode Available	1

Show:10 25 50

← PrevPage 203 of 271Next →

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