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 2301–2325 of 6771 papers

Title	Date	Tasks	Status	Hype
CoDream: Exchanging dreams instead of models for federated aggregation with heterogeneous models	Feb 25, 2024	Federated Learning	—Unverified	0
Bayesian Neural Network For Personalized Federated Learning Parameter Selection	Feb 25, 2024	Federated LearningPersonalized Federated Learning	—Unverified	0
ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devices	Feb 24, 2024	Federated Learning	—Unverified	0
FedMM: Federated Multi-Modal Learning with Modality Heterogeneity in Computational Pathology	Feb 24, 2024	Federated LearningPrivacy Preserving	—Unverified	0
Chu-ko-nu: A Reliable, Efficient, and Anonymously Authentication-Enabled Realization for Multi-Round Secure Aggregation in Federated Learning	Feb 23, 2024	Federated Learning	—Unverified	0
Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting	Feb 23, 2024	Federated Learning	CodeCode Available	1
A Bargaining-based Approach for Feature Trading in Vertical Federated Learning	Feb 23, 2024	Federated LearningVertical Federated Learning	—Unverified	0
Convergence Analysis of Split Federated Learning on Heterogeneous Data	Feb 23, 2024	Federated Learning	—Unverified	0
RVE-PFL: Robust Variational Encoder-based Personalised Federated Learning against Model Inversion Attacks	Feb 22, 2024	Federated LearningPrivacy Preserving	CodeCode Available	0
Practical Insights into Knowledge Distillation for Pre-Trained Models	Feb 22, 2024	Federated LearningKnowledge Distillation	—Unverified	0
Efficient Unbiased Sparsification	Feb 22, 2024	Federated Learning	—Unverified	0
Privacy-Enhancing Collaborative Information Sharing through Federated Learning -- A Case of the Insurance Industry	Feb 22, 2024	Federated LearningFraud Detection	—Unverified	0
Federated Fairness without Access to Sensitive Groups	Feb 22, 2024	FairnessFederated Learning	—Unverified	0
Federated Neural Graph Databases	Feb 22, 2024	Complex Query AnsweringFederated Learning	—Unverified	0
Federated Learning in Genetics: Extended Analysis of Accuracy, Performance and Privacy Trade-offs	Feb 22, 2024	Federated LearningPrognosis	—Unverified	0
FedADMM-InSa: An Inexact and Self-Adaptive ADMM for Federated Learning	Feb 21, 2024	Federated Learning	—Unverified	0
Random Aggregate Beamforming for Over-the-Air Federated Learning in Large-Scale Networks	Feb 20, 2024	Federated Learning	—Unverified	0
FGAD: Self-boosted Knowledge Distillation for An Effective Federated Graph Anomaly Detection Framework	Feb 20, 2024	Anomaly DetectionFederated Learning	—Unverified	0
Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates	Feb 20, 2024	Federated Learningimage-classification	—Unverified	0
Federated Multi-Task Learning on Non-IID Data Silos: An Experimental Study	Feb 20, 2024	Federated LearningMulti-Task Learning	CodeCode Available	0
Stochastic Approximation Approach to Federated Machine Learning	Feb 20, 2024	Federated Learning	—Unverified	0
ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning	Feb 19, 2024	Dimensionality ReductionFederated Learning	CodeCode Available	0
Federated Bayesian Network Ensembles	Feb 19, 2024	Federated Learning	—Unverified	0
Secure Federated Learning Across Heterogeneous Cloud and High-Performance Computing Resources -- A Case Study on Federated Fine-tuning of LLaMA 2	Feb 19, 2024	Cloud ComputingFederated Learning	—Unverified	0
On the Byzantine-Resilience of Distillation-Based Federated Learning	Feb 19, 2024	Federated LearningKnowledge Distillation	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