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 1201–1225 of 6771 papers

Title	Date	Tasks	Status	Hype
LanFL: Differentially Private Federated Learning with Large Language Models using Synthetic Samples	Oct 24, 2024	Federated LearningPrivacy Preserving	—Unverified	0
Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift	Oct 24, 2024	ClusteringFederated Learning	CodeCode Available	1
FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learning	Oct 24, 2024	ClusteringFederated Learning	—Unverified	0
Adapting MLOps for Diverse In-Network Intelligence in 6G Era: Challenges and Solutions	Oct 24, 2024	Federated LearningManagement	—Unverified	0
LEGO: Language Model Building Blocks	Oct 23, 2024	Federated LearningLanguage Modeling	—Unverified	0
Which Client is Reliable?: A Reliable and Personalized Prompt-based Federated Learning for Medical Image Question Answering	Oct 23, 2024	Federated LearningMedical Visual Question Answering	—Unverified	0
ProFL: Performative Robust Optimal Federated Learning	Oct 23, 2024	Federated Learning	—Unverified	0
Towards Active Participant-Centric Vertical Federated Learning: Some Representations May Be All You Need	Oct 23, 2024	AllFederated Learning	—Unverified	0
Securing Federated Learning against Backdoor Threats with Foundation Model Integration	Oct 23, 2024	Backdoor AttackFederated Learning	—Unverified	0
Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data	Oct 23, 2024	Entity AlignmentFederated Learning	CodeCode Available	1
Multi-Continental Healthcare Modelling Using Blockchain-Enabled Federated Learning	Oct 23, 2024	Federated LearningPrivacy Preserving	—Unverified	0
Enhancing Federated Learning Convergence with Dynamic Data Queue and Data Entropy-driven Participant Selection	Oct 23, 2024	Federated Learning	—Unverified	0
Meta Stackelberg Game: Robust Federated Learning against Adaptive and Mixed Poisoning Attacks	Oct 22, 2024	Federated LearningMeta-Learning	—Unverified	0
Deep Learning Aided Broadcast Codes with Feedback	Oct 22, 2024	Deep LearningFederated Learning	—Unverified	0
Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis	Oct 22, 2024	Causal InferenceFederated Learning	—Unverified	0
Data Obfuscation through Latent Space Projection (LSP) for Privacy-Preserving AI Governance: Case Studies in Medical Diagnosis and Finance Fraud Detection	Oct 22, 2024	AttributeFairness	—Unverified	0
Subword Embedding from Bytes Gains Privacy without Sacrificing Accuracy and Complexity	Oct 21, 2024	Federated LearningLanguage Modeling	—Unverified	0
Distributed Learning for UAV Swarms	Oct 21, 2024	Federated Learning	—Unverified	0
Extracting Spatiotemporal Data from Gradients with Large Language Models	Oct 21, 2024	Federated LearningLanguage Modelling	—Unverified	0
DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning Based on Constant-Overhead Linear Secret Resharing	Oct 21, 2024	Federated Learning	—Unverified	0
Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification	Oct 21, 2024	Federated LearningFew-Shot Learning	—Unverified	0
Geographical Node Clustering and Grouping to Guarantee Data IIDness in Federated Learning	Oct 21, 2024	ClusteringFederated Learning	—Unverified	0
A Bayesian Framework for Clustered Federated Learning	Oct 20, 2024	Federated Learning	—Unverified	0
Tighter Performance Theory of FedExProx	Oct 20, 2024	Distributed OptimizationDiversity	—Unverified	0
DPVS-Shapley:Faster and Universal Contribution Evaluation Component in Federated Learning	Oct 19, 2024	Contribution AssessmentFederated 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