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 2801–2825 of 6771 papers

Title	Date	Tasks	Status	Hype
Mobile Application for Oral Disease Detection using Federated Learning	Oct 27, 2023	Federated Learningobject-detection	—Unverified	0
Single-shot General Hyper-parameter Optimization for Federated Learning	Oct 27, 2023	Federated Learning	—Unverified	0
EcoLearn: Optimizing the Carbon Footprint of Federated Learning	Oct 27, 2023	Federated Learning	—Unverified	0
Federated Learning over Hierarchical Wireless Networks: Training Latency Minimization via Submodel Partitioning	Oct 27, 2023	Federated Learning	—Unverified	0
Contextual Stochastic Bilevel Optimization	Oct 27, 2023	Bilevel OptimizationFederated Learning	—Unverified	0
Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated Learning	Oct 27, 2023	Federated LearningPersonalized Federated Learning	CodeCode Available	1
Secure short-term load forecasting for smart grids with transformer-based federated learning	Oct 26, 2023	Deep LearningFederated Learning	—Unverified	0
Taming Gradient Variance in Federated Learning with Networked Control Variates	Oct 26, 2023	Federated Learning	—Unverified	0
Navigating Data Heterogeneity in Federated Learning A Semi-Supervised Federated Object Detection	Oct 26, 2023	Autonomous DrivingFederated Learning	CodeCode Available	1
FedPEAT: Convergence of Federated Learning, Parameter-Efficient Fine Tuning, and Emulator Assisted Tuning for Artificial Intelligence Foundation Models with Mobile Edge Computing	Oct 26, 2023	Deep Reinforcement LearningEdge-computing	—Unverified	0
Personalized Federated X -armed Bandit	Oct 25, 2023	Federated Learning	—Unverified	0
Information-Theoretic Generalization Analysis for Topology-aware Heterogeneous Federated Edge Learning over Noisy Channels	Oct 25, 2023	Federated Learning	—Unverified	0
FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning	Oct 25, 2023	Federated LearningLanguage Modeling	—Unverified	0
AirFL-Mem: Improving Communication-Learning Trade-Off by Long-Term Memory	Oct 25, 2023	Federated Learning	—Unverified	0
How Robust is Federated Learning to Communication Error? A Comparison Study Between Uplink and Downlink Channels	Oct 25, 2023	Federated LearningPrivacy Preserving	—Unverified	0
FLTrojan: Privacy Leakage Attacks against Federated Language Models Through Selective Weight Tampering	Oct 24, 2023	Federated LearningLanguage Modeling	—Unverified	0
Accelerating Split Federated Learning over Wireless Communication Networks	Oct 24, 2023	Federated Learning	—Unverified	0
ADMarker: A Multi-Modal Federated Learning System for Monitoring Digital Biomarkers of Alzheimer's Disease	Oct 23, 2023	Federated LearningPrivacy Preserving	—Unverified	0
Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms	Oct 23, 2023	Federated Learning	—Unverified	0
Vicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentation	Oct 23, 2023	Cardiac SegmentationData Augmentation	—Unverified	0
Serverless Federated Learning with flwr-serverless	Oct 23, 2023	Federated Learning	CodeCode Available	1
Dynamically Weighted Federated k-Means	Oct 23, 2023	ClusteringFederated Learning	CodeCode Available	0
Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization	Oct 23, 2023	Federated Learning	CodeCode Available	1
Federated learning compression designed for lightweight communications	Oct 23, 2023	Cloud ComputingFederated Learning	CodeCode Available	0
Coordinated Replay Sample Selection for Continual Federated Learning	Oct 23, 2023	Continual LearningFederated Learning	—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