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 4926–4950 of 6771 papers

Title	Date	Tasks	Status	Hype
Differential Secrecy for Distributed Data and Applications to Robust Differentially Secure Vector Summation	Feb 22, 2022	Federated Learning	—Unverified	0
Backdoor Defense in Federated Learning Using Differential Testing and Outlier Detection	Feb 21, 2022	backdoor defenseFederated Learning	—Unverified	0
Privacy Leakage of Adversarial Training Models in Federated Learning Systems	Feb 21, 2022	Federated Learning	CodeCode Available	1
BERT WEAVER: Using WEight AVERaging to enable lifelong learning for transformer-based models in biomedical semantic search engines	Feb 21, 2022	Federated LearningLifelong learning	CodeCode Available	0
Personalized Federated Learning with Exact Stochastic Gradient Descent	Feb 20, 2022	Federated LearningMulti-class Classification	—Unverified	0
Collusion Resistant Federated Learning with Oblivious Distributed Differential Privacy	Feb 20, 2022	Federated LearningPrivacy Preserving	—Unverified	0
Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learning	Feb 18, 2022	Federated Learning	—Unverified	0
Differentially Private Federated Learning via Inexact ADMM with Multiple Local Updates	Feb 18, 2022	Federated Learningimage-classification	—Unverified	0
FedEmbed: Personalized Private Federated Learning	Feb 18, 2022	Federated Learning	—Unverified	0
PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networks	Feb 18, 2022	Federated LearningPersonalized Federated Learning	—Unverified	0
ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!	Feb 18, 2022	Federated Learning	—Unverified	0
When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing	Feb 17, 2022	ClassificationCPU	—Unverified	0
FLAME: Federated Learning Across Multi-device Environments	Feb 17, 2022	Activity RecognitionFederated Learning	—Unverified	0
Cross-Silo Heterogeneous Model Federated Multitask Learning	Feb 17, 2022	Federated Learningmodel	CodeCode Available	0
LAMP: Extracting Text from Gradients with Language Model Priors	Feb 17, 2022	Federated LearningLanguage Modeling	CodeCode Available	1
Federated Stochastic Gradient Descent Begets Self-Induced Momentum	Feb 17, 2022	Federated Learning	—Unverified	0
Time-Correlated Sparsification for Efficient Over-the-Air Model Aggregation in Wireless Federated Learning	Feb 17, 2022	Federated LearningModel Compression	—Unverified	0
An Equivalence Between Data Poisoning and Byzantine Gradient Attacks	Feb 17, 2022	Data PoisoningFederated Learning	CodeCode Available	0
Capitalization Normalization for Language Modeling with an Accurate and Efficient Hierarchical RNN Model	Feb 16, 2022	Federated LearningLanguage Modeling	—Unverified	0
Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor Segmentation	Feb 16, 2022	Brain Tumor SegmentationFederated Learning	CodeCode Available	0
Single-shot Hyper-parameter Optimization for Federated Learning: A General Algorithm & Analysis	Feb 16, 2022	Federated Learning	—Unverified	0
Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams	Feb 16, 2022	Federated Learning	—Unverified	0
No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices	Feb 16, 2022	Federated LearningKnowledge Distillation	—Unverified	0
Federated-Learning-Based Anomaly Detection for IoT Security Attacks	Feb 15, 2022	Anomaly DetectionFederated Learning	—Unverified	0
Towards Verifiable Federated Learning	Feb 15, 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