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 5251–5275 of 6771 papers

Title	Date	Tasks	Status	Hype
FedFly: Towards Migration in Edge-based Distributed Federated Learning	Nov 2, 2021	Federated LearningPrivacy Preserving	CodeCode Available	1
Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Training	Nov 2, 2021	COVID-19 DiagnosisFederated Learning	—Unverified	0
Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories	Nov 1, 2021	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—Unverified	0
To Talk or to Work: Delay Efficient Federated Learning over Mobile Edge Devices	Nov 1, 2021	Edge-computingFederated Learning	—Unverified	0
Resource-Efficient Federated Learning	Nov 1, 2021	FairnessFederated Learning	CodeCode Available	1
FedFm: Towards a Robust Federated Learning Approach For Fault Mitigation at the Edge Nodes	Nov 1, 2021	Federated Learning	—Unverified	0
Robust Federated Learning via Over-The-Air Computation	Nov 1, 2021	Federated Learning	—Unverified	0
Implicit Model Specialization through DAG-based Decentralized Federated Learning	Nov 1, 2021	Federated Learning	CodeCode Available	1
Efficient passive membership inference attack in federated learning	Oct 31, 2021	Federated LearningInference Attack	CodeCode Available	1
DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning	Oct 31, 2021	Federated LearningPrivacy Preserving	—Unverified	0
Wireless Federated Learning over MIMO Networks: Joint Device Scheduling and Beamforming Design	Oct 31, 2021	Federated LearningScheduling	—Unverified	0
Revealing and Protecting Labels in Distributed Training	Oct 31, 2021	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	0
Dynamic Differential-Privacy Preserving SGD	Oct 30, 2021	Federated Learningimage-classification	—Unverified	0
Federated Semi-Supervised Learning with Class Distribution Mismatch	Oct 29, 2021	Federated Learning	—Unverified	0
Improving Fairness via Federated Learning	Oct 29, 2021	FairnessFederated Learning	CodeCode Available	1
Gradient Inversion with Generative Image Prior	Oct 28, 2021	Federated Learning	CodeCode Available	1
Towards Model Agnostic Federated Learning Using Knowledge Distillation	Oct 28, 2021	Federated LearningKnowledge Distillation	—Unverified	0
Federated Learning with Heterogeneous Differential Privacy	Oct 28, 2021	Federated LearningPrivacy Preserving	—Unverified	0
Communication-Efficient ADMM-based Federated Learning	Oct 28, 2021	Federated Learning	CodeCode Available	0
Differentially Private Federated Bayesian Optimization with Distributed Exploration	Oct 27, 2021	Bayesian OptimizationFederated Learning	—Unverified	0
FedPrune: Towards Inclusive Federated Learning	Oct 27, 2021	FairnessFederated Learning	—Unverified	0
Over-the-Air Aggregation for Federated Learning: Waveform Superposition and Prototype Validation	Oct 27, 2021	Federated Learning	—Unverified	0
Spatio-Temporal Federated Learning for Massive Wireless Edge Networks	Oct 27, 2021	Federated Learning	—Unverified	0
What Do We Mean by Generalization in Federated Learning?	Oct 27, 2021	Federated Learning	—Unverified	0
CAFE: Catastrophic Data Leakage in Vertical Federated Learning	Oct 26, 2021	Federated LearningVertical Federated Learning	CodeCode Available	1

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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