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 4376–4400 of 6771 papers

Title	Date	Tasks	Status	Hype
Personalizing or Not: Dynamically Personalized Federated Learning with Incentives	Aug 12, 2022	Federated LearningPersonalized Federated Learning	—Unverified	0
A Knowledge Distillation-Based Backdoor Attack in Federated Learning	Aug 12, 2022	Backdoor AttackFederated Learning	—Unverified	0
Dropout is NOT All You Need to Prevent Gradient Leakage	Aug 12, 2022	AllFederated Learning	CodeCode Available	0
A Fast Blockchain-based Federated Learning Framework with Compressed Communications	Aug 12, 2022	Federated Learning	—Unverified	0
A Modified UDP for Federated Learning Packet Transmissions	Aug 11, 2022	Federated Learning	—Unverified	0
Shielding Federated Learning Systems against Inference Attacks with ARM TrustZone	Aug 11, 2022	Federated Learning	—Unverified	0
FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning	Aug 10, 2022	Federated Learning	CodeCode Available	1
Fast Heterogeneous Federated Learning with Hybrid Client Selection	Aug 10, 2022	ClusteringFederated Learning	—Unverified	0
Application of federated learning in manufacturing	Aug 9, 2022	Federated Learning	—Unverified	0
Combining Stochastic Defenses to Resist Gradient Inversion: An Ablation Study	Aug 9, 2022	Federated LearningImage Classification	—Unverified	0
PEPPER: Empowering User-Centric Recommender Systems over Gossip Learning	Aug 9, 2022	Federated LearningMovie Recommendation	—Unverified	0
Towards Energy-Aware Federated Learning on Battery-Powered Clients	Aug 9, 2022	FairnessFederated Learning	CodeCode Available	0
Learning-Based Client Selection for Federated Learning Services Over Wireless Networks with Constrained Monetary Budgets	Aug 8, 2022	Deep Reinforcement LearningFederated Learning	—Unverified	0
Federated Adversarial Learning: A Framework with Convergence Analysis	Aug 7, 2022	Federated Learning	—Unverified	0
Low-Latency Cooperative Spectrum Sensing via Truncated Vertical Federated Learning	Aug 7, 2022	Federated LearningScheduling	—Unverified	0
Terahertz-Band Channel and Beam Split Estimation via Array Perturbation Model	Aug 7, 2022	Federated Learning	—Unverified	0
Distributed Contrastive Learning for Medical Image Segmentation	Aug 7, 2022	Contrastive LearningFederated Learning	—Unverified	0
Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions	Aug 5, 2022	DiagnosticFederated Learning	—Unverified	0
ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity	Aug 4, 2022	Federated Learning	—Unverified	0
FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning	Aug 4, 2022	Deep Reinforcement LearningFairness	—Unverified	0
Embedding Alignment for Unsupervised Federated Learning via Smart Data Exchange	Aug 4, 2022	Contrastive LearningFederated Learning	—Unverified	0
How Much Privacy Does Federated Learning with Secure Aggregation Guarantee?	Aug 3, 2022	Federated LearningPrivacy Preserving	—Unverified	0
A New Implementation of Federated Learning for Privacy and Security Enhancement	Aug 3, 2022	Federated Learning	—Unverified	0
Asynchronous Federated Learning for Edge-assisted Vehicular Networks	Aug 3, 2022	Federated Learning	CodeCode Available	1
Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning	Aug 2, 2022	Federated LearningPersonalized Federated Learning	—Unverified	0

Show:10 25 50

← PrevPage 176 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