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 2751–2775 of 6771 papers

Title	Date	Tasks	Status	Hype
Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency	Nov 9, 2023	Deep LearningFederated Learning	—Unverified	0
The Paradox of Noise: An Empirical Study of Noise-Infusion Mechanisms to Improve Generalization, Stability, and Privacy in Federated Learning	Nov 9, 2023	Federated LearningPrivacy Preserving	—Unverified	0
Data Valuation and Detections in Federated Learning	Nov 9, 2023	Data ValuationFederated Learning	CodeCode Available	1
Edge-assisted U-Shaped Split Federated Learning with Privacy-preserving for Internet of Things	Nov 8, 2023	Federated LearningPrivacy Preserving	—Unverified	0
Accurate Autism Spectrum Disorder prediction using Support Vector Classifier based on Federated Learning (SVCFL)	Nov 8, 2023	DiagnosticFederated Learning	—Unverified	0
Decentralized Personalized Online Federated Learning	Nov 8, 2023	Federated Learning	—Unverified	0
Asynchronous Message-Passing and Zeroth-Order Optimization Based Distributed Learning with a Use-Case in Resource Allocation in Communication Networks	Nov 8, 2023	Distributed OptimizationFederated Learning	—Unverified	0
Robust and Communication-Efficient Federated Domain Adaptation via Random Features	Nov 8, 2023	Domain AdaptationFederated Learning	CodeCode Available	0
Cross-Silo Federated Learning Across Divergent Domains with Iterative Parameter Alignment	Nov 8, 2023	FairnessFederated Learning	CodeCode Available	0
Input Reconstruction Attack against Vertical Federated Large Language Models	Nov 7, 2023	Federated LearningGPU	—Unverified	0
Blind Federated Learning via Over-the-Air q-QAM	Nov 7, 2023	Federated Learning	—Unverified	0
Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networks	Nov 7, 2023	Federated Learning	—Unverified	0
SaFL: Sybil-aware Federated Learning with Application to Face Recognition	Nov 7, 2023	Face RecognitionFederated Learning	—Unverified	0
Convergence Analysis of Sequential Federated Learning on Heterogeneous Data	Nov 6, 2023	Federated Learning	CodeCode Available	0
CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centers	Nov 6, 2023	Federated Learning	—Unverified	0
Asynchronous Local Computations in Distributed Bayesian Learning	Nov 6, 2023	Federated Learning	—Unverified	0
Goal-Oriented Wireless Communication Resource Allocation for Cyber-Physical Systems	Nov 6, 2023	Decision MakingDistributed Optimization	—Unverified	0
Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches	Nov 6, 2023	Federated LearningPrivacy Preserving	—Unverified	0
Communication Efficient and Privacy-Preserving Federated Learning Based on Evolution Strategies	Nov 5, 2023	Federated LearningPrivacy Preserving	CodeCode Available	0
Sample Complexity of Linear Regression Models for Opinion Formation in Networks	Nov 4, 2023	Federated Learningregression	CodeCode Available	0
Hybrid quantum image classification and federated learning for hepatic steatosis diagnosis	Nov 4, 2023	DiagnosticFederated Learning	—Unverified	0
Client Orchestration and Cost-Efficient Joint Optimization for NOMA-Enabled Hierarchical Federated Learning	Nov 3, 2023	Federated LearningProblem Decomposition	—Unverified	0
Epidemic Decision-making System Based Federated Reinforcement Learning	Nov 3, 2023	Decision MakingFederated Learning	—Unverified	0
Heterogeneous federated collaborative filtering using FAIR: Federated Averaging in Random Subspaces	Nov 3, 2023	Collaborative FilteringFederated Learning	CodeCode Available	0
Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization	Nov 2, 2023	Federated 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