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 4576–4600 of 6771 papers

Title	Date	Tasks	Status	Hype
Leveraging Centric Data Federated Learning Using Blockchain For Integrity Assurance	Jun 9, 2022	Federated LearningUser Simulation	—Unverified	0
Accelerating Asynchronous Federated Learning Convergence via Opportunistic Mobile Relaying	Jun 9, 2022	AttributeFederated Learning	—Unverified	0
HideNseek: Federated Lottery Ticket via Server-side Pruning and Sign Supermask	Jun 9, 2022	Federated LearningModel Compression	—Unverified	0
On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data	Jun 9, 2022	Federated Learning	—Unverified	0
Gradient Obfuscation Gives a False Sense of Security in Federated Learning	Jun 8, 2022	Federated Learningimage-classification	—Unverified	0
Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT Imaging	Jun 8, 2022	Computed Tomography (CT)CT Reconstruction	CodeCode Available	1
pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning	Jun 8, 2022	FairnessFederated Learning	—Unverified	0
FedHPO-B: A Benchmark Suite for Federated Hyperparameter Optimization	Jun 8, 2022	BenchmarkingFederated Learning	—Unverified	0
Subject Membership Inference Attacks in Federated Learning	Jun 7, 2022	Federated Learning	—Unverified	0
FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning	Jun 7, 2022	Ensemble LearningFederated Learning	—Unverified	0
FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning	Jun 7, 2022	FairnessFederated Learning	CodeCode Available	0
An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning	Jun 7, 2022	Federated LearningGraph Learning	—Unverified	0
Privacy Amplification via Shuffled Check-Ins	Jun 7, 2022	Federated Learning	CodeCode Available	1
Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation	Jun 7, 2022	Federated LearningVocal Bursts Type Prediction	—Unverified	0
FedPop: A Bayesian Approach for Personalised Federated Learning	Jun 7, 2022	Federated LearningUncertainty Quantification	—Unverified	0
A Benchmark for Federated Hetero-Task Learning	Jun 7, 2022	Federated LearningMeta-Learning	—Unverified	0
Subject Granular Differential Privacy in Federated Learning	Jun 7, 2022	Federated Learning	—Unverified	0
Group privacy for personalized federated learning	Jun 7, 2022	FairnessFederated Learning	—Unverified	0
FedNST: Federated Noisy Student Training for Automatic Speech Recognition	Jun 6, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Pretrained Models for Multilingual Federated Learning	Jun 6, 2022	Federated LearningLanguage Modeling	CodeCode Available	1
Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning	Jun 6, 2022	Federated Learning	CodeCode Available	1
Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networks	Jun 6, 2022	Federated LearningManagement	—Unverified	0
Generalized Federated Learning via Sharpness Aware Minimization	Jun 6, 2022	Federated LearningPrivacy Preserving	CodeCode Available	0
Certified Robustness in Federated Learning	Jun 6, 2022	Federated Learning	CodeCode Available	0
An Optimal Transport Approach to Personalized Federated Learning	Jun 6, 2022	Federated LearningPersonalized 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