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 5276–5300 of 6771 papers

Title	Date	Tasks	Status	Hype
Ensemble Federated Adversarial Training with Non-IID data	Oct 26, 2021	DiversityFederated Learning	—Unverified	0
Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee	Oct 26, 2021	Decision MakingFederated Learning	CodeCode Available	1
Semi-Supervised Federated Learning with non-IID Data: Algorithm and System Design	Oct 26, 2021	Federated Learning	—Unverified	0
DPCOVID: Privacy-Preserving Federated Covid-19 Detection	Oct 26, 2021	Federated LearningPrivacy Preserving	—Unverified	0
FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective	Oct 26, 2021	Federated LearningModel Poisoning	CodeCode Available	1
MarS-FL: Enabling Competitors to Collaborate in Federated Learning	Oct 26, 2021	Decision MakingFederated Learning	—Unverified	0
Qu-ANTI-zation: Exploiting Quantization Artifacts for Achieving Adversarial Outcomes	Oct 26, 2021	Backdoor AttackFederated Learning	CodeCode Available	1
Optimal Model Averaging: Towards Personalized Collaborative Learning	Oct 25, 2021	Federated Learningmodel	—Unverified	0
Federated Test-Time Adaptive Face Presentation Attack Detection with Dual-Phase Privacy Preservation	Oct 25, 2021	Face Presentation Attack DetectionFace Recognition	—Unverified	0
Optimization-Based GenQSGD for Federated Edge Learning	Oct 25, 2021	Edge-computingFederated Learning	—Unverified	0
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models	Oct 25, 2021	Federated Learning	CodeCode Available	1
Federated Multiple Label Hashing (FedMLH): Communication Efficient Federated Learning on Extreme Classification Tasks	Oct 23, 2021	ClassificationFederated Learning	—Unverified	0
Game of Gradients: Mitigating Irrelevant Clients in Federated Learning	Oct 23, 2021	Federated Learningimage-classification	CodeCode Available	0
MANDERA: Malicious Node Detection in Federated Learning via Ranking	Oct 22, 2021	Federated Learning	—Unverified	0
PRECAD: Privacy-Preserving and Robust Federated Learning via Crypto-Aided Differential Privacy	Oct 22, 2021	Federated LearningModel Poisoning	—Unverified	0
WebFed: Cross-platform Federated Learning Framework Based on Web Browser with Local Differential Privacy	Oct 22, 2021	Federated Learning	—Unverified	0
Federated Learning over Wireless IoT Networks with Optimized Communication and Resources	Oct 22, 2021	Federated LearningScheduling	—Unverified	0
Tackling the Local Bias in Federated Graph Learning	Oct 22, 2021	Federated LearningGraph Learning	—Unverified	0
Federated Unlearning via Class-Discriminative Pruning	Oct 22, 2021	Federated Learningimage-classification	CodeCode Available	0
DistFL: Distribution-aware Federated Learning for Mobile Scenarios	Oct 22, 2021	Federated LearningPrivacy Preserving	CodeCode Available	1
Bristle: Decentralized Federated Learning in Byzantine, Non-i.i.d. Environments	Oct 21, 2021	Federated LearningTransfer Learning	—Unverified	0
Boosting Resource-Constrained Federated Learning Systems with Guessed Updates	Oct 21, 2021	Federated Learning	—Unverified	0
FedGEMS: Federated Learning of Larger Server Models via Selective Knowledge Fusion	Oct 21, 2021	Federated Learningimage-classification	—Unverified	0
PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion	Oct 21, 2021	Federated LearningModel Poisoning	—Unverified	0
SecureBoost+: Large Scale and High-Performance Vertical Federated Gradient Boosting Decision Tree	Oct 21, 2021	Federated LearningPrivacy Preserving	—Unverified	0

Show:10 25 50

← PrevPage 212 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