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 4076–4100 of 6771 papers

Title	Date	Tasks	Status	Hype
Adaptive Federated Minimax Optimization with Lower Complexities	Nov 14, 2022	Federated LearningPrivacy Preserving	—Unverified	0
Watermarking in Secure Federated Learning: A Verification Framework Based on Client-Side Backdooring	Nov 14, 2022	Federated Learning	—Unverified	0
FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model	Nov 14, 2022	Continual LearningFederated Learning	—Unverified	0
Differentially Private Vertical Federated Learning	Nov 13, 2022	Federated LearningVertical Federated Learning	—Unverified	0
Towards Privacy-Aware Causal Structure Learning in Federated Setting	Nov 13, 2022	Federated LearningPrivacy Preserving	CodeCode Available	0
Quantum Split Neural Network Learning using Cross-Channel Pooling	Nov 12, 2022	Federated LearningPrivacy Preserving	—Unverified	0
Federated Unsupervised Visual Representation Learning via Exploiting General Content and Personal Style	Nov 11, 2022	Contrastive LearningFederated Learning	—Unverified	0
A Federated Approach to Predicting Emojis in Hindi Tweets	Nov 11, 2022	Federated Learning	CodeCode Available	0
From Competition to Collaboration: Making Toy Datasets on Kaggle Clinically Useful for Chest X-Ray Diagnosis Using Federated Learning	Nov 11, 2022	Federated LearningPneumonia Detection	—Unverified	0
FedLesScan: Mitigating Stragglers in Serverless Federated Learning	Nov 10, 2022	Federated Learning	CodeCode Available	1
Secure Aggregation Is Not All You Need: Mitigating Privacy Attacks with Noise Tolerance in Federated Learning	Nov 10, 2022	AllFederated Learning	—Unverified	0
Robust Smart Home Face Recognition under Starving Federated Data	Nov 10, 2022	Adversarial AttackFace Recognition	CodeCode Available	0
Robust Federated Learning against both Data Heterogeneity and Poisoning Attack via Aggregation Optimization	Nov 10, 2022	Federated Learning	—Unverified	0
Warmup and Transfer Knowledge-Based Federated Learning Approach for IoT Continuous Authentication	Nov 10, 2022	Federated LearningTransfer Learning	—Unverified	0
Resource-Aware Heterogeneous Federated Learning using Neural Architecture Search	Nov 9, 2022	Federated LearningNeural Architecture Search	—Unverified	0
Almost Tight Error Bounds on Differentially Private Continual Counting	Nov 9, 2022	Federated Learning	—Unverified	0
Knowledge Distillation for Federated Learning: a Practical Guide	Nov 9, 2022	Federated LearningKnowledge Distillation	—Unverified	0
Framework Construction of an Adversarial Federated Transfer Learning Classifier	Nov 9, 2022	ClassificationDiagnostic	—Unverified	0
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design	Nov 8, 2022	Federated Learning	CodeCode Available	1
Clustered Federated Learning based on Nonconvex Pairwise Fusion	Nov 8, 2022	Federated Learning	CodeCode Available	0
Enhancing Efficiency in Multidevice Federated Learning through Data Selection	Nov 8, 2022	Federated Learning	CodeCode Available	1
Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Design	Nov 8, 2022	Federated LearningPrivacy Preserving	—Unverified	0
A Penalty-Based Method for Communication-Efficient Decentralized Bilevel Programming	Nov 8, 2022	Bilevel OptimizationFederated Learning	—Unverified	0
Federated Learning Using Three-Operator ADMM	Nov 8, 2022	Federated Learning	—Unverified	0
FedGrad: Optimisation in Decentralised Machine Learning	Nov 7, 2022	Federated 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