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 3126–3150 of 6771 papers

Title	Date	Tasks	Status	Hype
Cross-Silo Prototypical Calibration for Federated Learning with Non-IID Data	Aug 7, 2023	Contrastive LearningFederated Learning	CodeCode Available	1
Adapter-based Selective Knowledge Distillation for Federated Multi-domain Meeting Summarization	Aug 7, 2023	Federated LearningKnowledge Distillation	—Unverified	0
Communication-Efficient Framework for Distributed Image Semantic Wireless Transmission	Aug 7, 2023	Federated LearningSemantic Communication	—Unverified	0
Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining	Aug 6, 2023	Federated LearningStochastic Optimization	CodeCode Available	0
Private Federated Learning with Dynamic Power Control via Non-Coherent Over-the-Air Computation	Aug 5, 2023	Federated Learning	—Unverified	0
Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity	Aug 4, 2023	Federated LearningScheduling	—Unverified	0
SureFED: Robust Federated Learning via Uncertainty-Aware Inward and Outward Inspection	Aug 4, 2023	Federated Learningimage-classification	—Unverified	0
Label Inference Attacks against Node-level Vertical Federated GNNs	Aug 4, 2023	Federated LearningNode Classification	—Unverified	0
Scaling Survival Analysis in Healthcare with Federated Survival Forests: A Comparative Study on Heart Failure and Breast Cancer Genomics	Aug 4, 2023	Federated LearningSurvival Analysis	—Unverified	0
Federated Learning: Organizational Opportunities, Challenges, and Adoption Strategies	Aug 4, 2023	Federated Learning	—Unverified	0
Federated Representation Learning for Automatic Speech Recognition	Aug 3, 2023	Automatic Speech RecognitionFederated Learning	—Unverified	0
Hierarchical Federated Learning in Wireless Networks: Pruning Tackles Bandwidth Scarcity and System Heterogeneity	Aug 3, 2023	CPUFederated Learning	—Unverified	0
Dynamic Privacy Allocation for Locally Differentially Private Federated Learning with Composite Objectives	Aug 2, 2023	Federated Learning	—Unverified	0
Compressed and distributed least-squares regression: convergence rates with applications to Federated Learning	Aug 2, 2023	Federated Learningregression	—Unverified	0
Revolutionizing Wireless Networks with Federated Learning: A Comprehensive Review	Aug 1, 2023	Federated LearningScheduling	—Unverified	0
Differential Privacy for Adaptive Weight Aggregation in Federated Tumor Segmentation	Aug 1, 2023	BenchmarkingBrain Tumor Segmentation	—Unverified	0
Physics-Driven Spectrum-Consistent Federated Learning for Palmprint Verification	Aug 1, 2023	Federated Learning	CodeCode Available	1
Data Collaboration Analysis applied to Compound Datasets and the Introduction of Projection data to Non-IID settings	Aug 1, 2023	Federated Learning	—Unverified	0
Asynchronous Federated Learning with Bidirectional Quantized Communications and Buffered Aggregation	Aug 1, 2023	Federated LearningQuantization	—Unverified	0
AQUILA: Communication Efficient Federated Learning with Adaptive Quantization in Device Selection Strategy	Aug 1, 2023	Federated LearningPrivacy Preserving	—Unverified	0
Federated Learning for Data and Model Heterogeneity in Medical Imaging	Jul 31, 2023	Federated LearningKnowledge Distillation	—Unverified	0
Efficient Federated Learning via Local Adaptive Amended Optimizer with Linear Speedup	Jul 30, 2023	Federated Learning	—Unverified	0
Proof-of-Federated-Learning-Subchain: Free Partner Selection Subchain Based on Federated Learning	Jul 30, 2023	Deep LearningFederated Learning	—Unverified	0
Shuffled Differentially Private Federated Learning for Time Series Data Analytics	Jul 30, 2023	Activity RecognitionFederated Learning	—Unverified	0
SemiSFL: Split Federated Learning on Unlabeled and Non-IID Data	Jul 29, 2023	ClusteringFederated 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