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 5701–5725 of 6771 papers

Title	Date	Tasks	Status	Hype
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing	Jun 8, 2021	Federated LearningHyperparameter Optimization	—Unverified	0
FedNILM: Applying Federated Learning to NILM Applications at the Edge	Jun 7, 2021	Federated LearningModel Compression	—Unverified	0
Unsupervised Clustered Federated Learning in Complex Multi-source Acoustic Environments	Jun 7, 2021	ClusteringFederated Learning	CodeCode Available	0
Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning	Jun 7, 2021	FairnessFederated Learning	—Unverified	0
Preservation of the Global Knowledge by Not-True Distillation in Federated Learning	Jun 6, 2021	Continual LearningFederated Learning	CodeCode Available	1
FedNL: Making Newton-Type Methods Applicable to Federated Learning	Jun 5, 2021	Federated LearningModel Compression	—Unverified	0
FedBABU: Towards Enhanced Representation for Federated Image Classification	Jun 4, 2021	ClassificationFederated Learning	CodeCode Available	1
SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks	Jun 4, 2021	BIG-bench Machine LearningFederated Learning	CodeCode Available	1
FedCCEA : A Practical Approach of Client Contribution Evaluation for Federated Learning	Jun 4, 2021	Data ValuationFederated Learning	—Unverified	0
Local Adaptivity in Federated Learning: Convergence and Consistency	Jun 4, 2021	Federated Learning	—Unverified	0
Federated Neural Collaborative Filtering	Jun 2, 2021	Collaborative FilteringFederated Learning	—Unverified	0
SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training	Jun 2, 2021	Data AugmentationFederated Learning	CodeCode Available	1
FedHealth 2: Weighted Federated Transfer Learning via Batch Normalization for Personalized Healthcare	Jun 2, 2021	Activity RecognitionFederated Learning	—Unverified	0
An Investigation towards Differentially Private Sequence Tagging in a Federated Framework	Jun 1, 2021	Federated Learningnamed-entity-recognition	—Unverified	0
Understanding Unintended Memorization in Language Models Under Federated Learning	Jun 1, 2021	Federated LearningMemorization	—Unverified	0
QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learning	Jun 1, 2021	Federated Learning	—Unverified	0
Wireless Federated Learning with Limited Communication and Differential Privacy	Jun 1, 2021	Dimensionality ReductionFederated Learning	—Unverified	0
H-FL: A Hierarchical Communication-Efficient and Privacy-Protected Architecture for Federated Learning	Jun 1, 2021	Federated Learning	—Unverified	0
Towards a Federated Learning Framework for Heterogeneous Devices of Internet of Things	May 31, 2021	Federated Learning	—Unverified	0
Federated Learning for Industrial Internet of Things in Future Industries	May 31, 2021	Federated Learning	—Unverified	0
Meta-HAR: Federated Representation Learning for Human Activity Recognition	May 31, 2021	Activity PredictionActivity Recognition	CodeCode Available	1
Unifying Distillation with Personalization in Federated Learning	May 31, 2021	FairnessFederated Learning	—Unverified	0
Quantum Federated Learning with Quantum Data	May 30, 2021	BIG-bench Machine LearningFederated Learning	CodeCode Available	1
CDMA: A Practical Cross-Device Federated Learning Algorithm for General Minimax Problems	May 29, 2021	Federated LearningGenerative Adversarial Network	CodeCode Available	0
Concept drift detection and adaptation for federated and continual learning	May 27, 2021	Continual LearningDrift Detection	—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