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 3176–3200 of 6771 papers

Title	Date	Tasks	Status	Hype
Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition	Jul 21, 2023	FairnessFederated Learning	CodeCode Available	0
Mitigating Communications Threats in Decentralized Federated Learning through Moving Target Defense	Jul 21, 2023	CPUFederated Learning	CodeCode Available	1
Training Latency Minimization for Model-Splitting Allowed Federated Edge Learning	Jul 21, 2023	Edge-computingFederated Learning	—Unverified	0
MAS: Towards Resource-Efficient Federated Multiple-Task Learning	Jul 21, 2023	AllFederated Learning	—Unverified	0
FedAutoMRI: Federated Neural Architecture Search for MR Image Reconstruction	Jul 21, 2023	Federated LearningImage Reconstruction	—Unverified	0
SecureBoost Hyperparameter Tuning via Multi-Objective Federated Learning	Jul 20, 2023	Federated LearningPrivacy Preserving	—Unverified	0
FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model Interpolation	Jul 20, 2023	Federated Learningimage-classification	CodeCode Available	1
An In-Depth Evaluation of Federated Learning on Biomedical Natural Language Processing	Jul 20, 2023	Federated LearningOne-Shot Learning	CodeCode Available	1
A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency	Jul 20, 2023	Federated LearningPrivacy Preserving	—Unverified	0
Communication-Efficient Federated Learning over Capacity-Limited Wireless Networks	Jul 20, 2023	Federated LearningQuantization	—Unverified	0
Private Federated Learning with Autotuned Compression	Jul 20, 2023	Federated Learning	—Unverified	0
Boosting Federated Learning Convergence with Prototype Regularization	Jul 20, 2023	Federated Learning	—Unverified	0
Heterogeneous Federated Learning: State-of-the-art and Research Challenges	Jul 20, 2023	Federated LearningSurvey	CodeCode Available	1
Fairness-Aware Client Selection for Federated Learning	Jul 20, 2023	FairnessFederated Learning	—Unverified	0
Blockchain-Based Federated Learning: Incentivizing Data Sharing and Penalizing Dishonest Behavior	Jul 19, 2023	FairnessFederated Learning	—Unverified	0
Graph Federated Learning Based on the Decentralized Framework	Jul 19, 2023	Federated LearningGraph Learning	—Unverified	0
Eliminating Label Leakage in Tree-Based Vertical Federated Learning	Jul 19, 2023	Federated LearningInference Attack	—Unverified	0
Learner Referral for Cost-Effective Federated Learning Over Hierarchical IoT Networks	Jul 19, 2023	FairnessFederated Learning	—Unverified	0
FedBug: A Bottom-Up Gradual Unfreezing Framework for Federated Learning	Jul 19, 2023	Federated Learning	CodeCode Available	0
Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning	Jul 18, 2023	Federated LearningLanguage Modeling	CodeCode Available	1
Federated Learning for Computationally-Constrained Heterogeneous Devices: A Survey	Jul 18, 2023	Federated LearningPrivacy Preserving	—Unverified	0
Mobility-Aware Joint User Scheduling and Resource Allocation for Low Latency Federated Learning	Jul 18, 2023	Federated LearningScheduling	—Unverified	0
A Federated learning model for Electric Energy management using Blockchain Technology	Jul 18, 2023	energy managementFederated Learning	—Unverified	0
Explanation-Guided Fair Federated Learning for Transparent 6G RAN Slicing	Jul 18, 2023	Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)	—Unverified	0
Integration of Large Language Models and Federated Learning	Jul 18, 2023	Federated LearningLanguage Modelling	—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