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 5876–5900 of 6771 papers

Title	Date	Tasks	Status
Dynamic Gradient Aggregation for Federated Domain Adaptation	Jun 14, 2021	Domain AdaptationFederated Learning	—Unverified
Heterogeneous Federated Learning using Dynamic Model Pruning and Adaptive Gradient	Jun 13, 2021	Federated LearningModel Compression	—Unverified
On the Convergence of Differentially Private Federated Learning on Non-Lipschitz Objectives, and with Normalized Client Updates	Jun 13, 2021	BenchmarkingFederated Learning	—Unverified
Understanding the Interplay between Privacy and Robustness in Federated Learning	Jun 13, 2021	Adversarial RobustnessFederated Learning	—Unverified
FGLP: A Federated Fine-Grained Location Prediction System for Mobile Users	Jun 13, 2021	Federated LearningPrediction	—Unverified
Joint Client Scheduling and Resource Allocation under Channel Uncertainty in Federated Learning	Jun 12, 2021	Federated LearningScheduling	—Unverified
Federated Learning on Non-IID Data: A Survey	Jun 12, 2021	BIG-bench Machine LearningFederated Learning	—Unverified
Federated Learning with Buffered Asynchronous Aggregation	Jun 11, 2021	Federated LearningPrivacy Preserving	—Unverified
Differentially Private Federated Learning via Inexact ADMM	Jun 11, 2021	Federated Learningimage-classification	—Unverified
Vertical Federated Learning without Revealing Intersection Membership	Jun 10, 2021	AllFederated Learning	—Unverified
Multi-VFL: A Vertical Federated Learning System for Multiple Data and Label Owners	Jun 10, 2021	Federated LearningVertical Federated Learning	—Unverified
Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning	Jun 9, 2021	Continual LearningFederated Learning	CodeCode Available
FedDICE: A ransomware spread detection in a distributed integrated clinical environment using federated learning and SDN based mitigation	Jun 9, 2021	Federated LearningPrivacy Preserving	—Unverified
No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data	Jun 9, 2021	Classifier calibrationFederated Learning	CodeCode Available
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing	Jun 8, 2021	Federated LearningHyperparameter Optimization	—Unverified
FedNILM: Applying Federated Learning to NILM Applications at the Edge	Jun 7, 2021	Federated LearningModel Compression	—Unverified
Unsupervised Clustered Federated Learning in Complex Multi-source Acoustic Environments	Jun 7, 2021	ClusteringFederated Learning	CodeCode Available
Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning	Jun 7, 2021	FairnessFederated Learning	—Unverified
FedNL: Making Newton-Type Methods Applicable to Federated Learning	Jun 5, 2021	Federated LearningModel Compression	—Unverified
FedCCEA : A Practical Approach of Client Contribution Evaluation for Federated Learning	Jun 4, 2021	Data ValuationFederated Learning	—Unverified
Local Adaptivity in Federated Learning: Convergence and Consistency	Jun 4, 2021	Federated Learning	—Unverified
FedHealth 2: Weighted Federated Transfer Learning via Batch Normalization for Personalized Healthcare	Jun 2, 2021	Activity RecognitionFederated Learning	—Unverified
Federated Neural Collaborative Filtering	Jun 2, 2021	Collaborative FilteringFederated Learning	—Unverified
Wireless Federated Learning with Limited Communication and Differential Privacy	Jun 1, 2021	Dimensionality ReductionFederated Learning	—Unverified
H-FL: A Hierarchical Communication-Efficient and Privacy-Protected Architecture for Federated Learning	Jun 1, 2021	Federated Learning	—Unverified

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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