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 3276–3300 of 6771 papers

Title	Date	Tasks	Status	Hype
Privacy and Fairness in Federated Learning: on the Perspective of Trade-off	Jun 25, 2023	FairnessFederated Learning	—Unverified	0
A Safe Genetic Algorithm Approach for Energy Efficient Federated Learning in Wireless Communication Networks	Jun 25, 2023	Federated Learning	—Unverified	0
Exploring Data Redundancy in Real-world Image Classification through Data Selection	Jun 25, 2023	Active LearningContinual Learning	CodeCode Available	0
Private Networked Federated Learning for Nonsmooth Objectives	Jun 24, 2023	Federated LearningPrivacy Preserving	—Unverified	0
An Intelligent Mechanism for Monitoring and Detecting Intrusions in IoT Devices	Jun 23, 2023	Federated LearningIntrusion Detection	—Unverified	0
Blockchain-based Federated Learning for Decentralized Energy Management Systems	Jun 23, 2023	energy managementenergy trading	—Unverified	0
A First Order Meta Stackelberg Method for Robust Federated Learning	Jun 23, 2023	Federated LearningMeta-Learning	—Unverified	0
FedSelect: Customized Selection of Parameters for Fine-Tuning during Personalized Federated Learning	Jun 23, 2023	Federated LearningPersonalized Federated Learning	—Unverified	0
Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity	Jun 23, 2023	Federated Learning	CodeCode Available	0
Enhancing Reliability in Federated mmWave Networks: A Practical and Scalable Solution using Radar-Aided Dynamic Blockage Recognition	Jun 22, 2023	Federated Learning	—Unverified	0
Reinforcement Federated Learning Method Based on Adaptive OPTICS Clustering	Jun 22, 2023	ClusteringFederated Learning	—Unverified	0
Decentralized Online Federated G-Network Learning for Lightweight Intrusion Detection	Jun 22, 2023	Federated LearningIntrusion Detection	—Unverified	0
Adaptive Compression in Federated Learning via Side Information	Jun 22, 2023	Federated Learning	CodeCode Available	0
An Efficient Virtual Data Generation Method for Reducing Communication in Federated Learning	Jun 21, 2023	Federated Learning	—Unverified	0
Split Learning in 6G Edge Networks	Jun 21, 2023	Edge-computingFederated Learning	—Unverified	0
FLGo: A Fully Customizable Federated Learning Platform	Jun 21, 2023	Federated Learning	—Unverified	0
Timely Asynchronous Hierarchical Federated Learning: Age of Convergence	Jun 21, 2023	Federated Learning	—Unverified	0
MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updates	Jun 21, 2023	Federated LearningPrivacy Preserving	CodeCode Available	0
Personalized Federated Learning with Feature Alignment and Classifier Collaboration	Jun 20, 2023	Federated LearningPersonalized Federated Learning	CodeCode Available	1
Decentralized Quantum Federated Learning for Metaverse: Analysis, Design and Implementation	Jun 20, 2023	Federated Learning	CodeCode Available	0
Randomized Quantization is All You Need for Differential Privacy in Federated Learning	Jun 20, 2023	AllFederated Learning	CodeCode Available	0
FedNoisy: Federated Noisy Label Learning Benchmark	Jun 20, 2023	Federated LearningLearning with noisy labels	CodeCode Available	1
Practical and General Backdoor Attacks against Vertical Federated Learning	Jun 19, 2023	Federated LearningVertical Federated Learning	—Unverified	0
Differentially Private Over-the-Air Federated Learning Over MIMO Fading Channels	Jun 19, 2023	Federated LearningPrivacy Preserving	—Unverified	0
Adaptive Federated Learning with Auto-Tuned Clients	Jun 19, 2023	Federated Learning	CodeCode Available	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