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 3651–3675 of 6771 papers

Title	Date	Tasks	Status
Federated Learning for Computer Vision	Aug 24, 2023	Federated Learning	—Unverified
ULDP-FL: Federated Learning with Across Silo User-Level Differential Privacy	Aug 23, 2023	Federated Learning	CodeCode Available
Unsupervised anomalies detection in IIoT edge devices networks using federated learning	Aug 23, 2023	Federated LearningIntrusion Detection	—Unverified
PFL-GAN: When Client Heterogeneity Meets Generative Models in Personalized Federated Learning	Aug 23, 2023	Federated LearningGenerative Adversarial Network	—Unverified
When MiniBatch SGD Meets SplitFed Learning:Convergence Analysis and Performance Evaluation	Aug 23, 2023	Federated Learning	—Unverified
Internal Cross-layer Gradients for Extending Homogeneity to Heterogeneity in Federated Learning	Aug 22, 2023	Federated Learning	CodeCode Available
Federated Learning in Big Model Era: Domain-Specific Multimodal Large Models	Aug 22, 2023	Federated LearningManagement	—Unverified
EM for Mixture of Linear Regression with Clustered Data	Aug 22, 2023	Federated Learningregression	—Unverified
Federated Learning on Patient Data for Privacy-Protecting Polycystic Ovary Syndrome Treatment	Aug 22, 2023	Federated Learning	CodeCode Available
Protect Federated Learning Against Backdoor Attacks via Data-Free Trigger Generation	Aug 22, 2023	Backdoor AttackFederated Learning	—Unverified
Federated Learning Robust to Byzantine Attacks: Achieving Zero Optimality Gap	Aug 21, 2023	Federated Learning	—Unverified
Split Learning for Distributed Collaborative Training of Deep Learning Models in Health Informatics	Aug 21, 2023	Computational EfficiencyDeep Learning	—Unverified
Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges	Aug 21, 2023	Federated Learning	—Unverified
A Safe Deep Reinforcement Learning Approach for Energy Efficient Federated Learning in Wireless Communication Networks	Aug 21, 2023	Deep Reinforcement LearningFederated Learning	—Unverified
Rethinking Client Drift in Federated Learning: A Logit Perspective	Aug 20, 2023	Federated Learning	—Unverified
Preserving Specificity in Federated Graph Learning for fMRI-based Neurological Disorder Identification	Aug 20, 2023	Federated LearningFunctional Connectivity	—Unverified
Federated Pseudo Modality Generation for Incomplete Multi-Modal MRI Reconstruction	Aug 20, 2023	Federated LearningMRI Reconstruction	—Unverified
Defending Label Inference Attacks in Split Learning under Regression Setting	Aug 18, 2023	Federated LearningPrivacy Preserving	—Unverified
Controlling Federated Learning for Covertness	Aug 17, 2023	Federated Learning	—Unverified
Over-the-Air Computation Aided Federated Learning with the Aggregation of Normalized Gradient	Aug 17, 2023	Federated Learning	—Unverified
Joint Power Control and Data Size Selection for Over-the-Air Computation Aided Federated Learning	Aug 17, 2023	Federated Learning	CodeCode Available
Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Data	Aug 17, 2023	Federated Learning	—Unverified
Stochastic Controlled Averaging for Federated Learning with Communication Compression	Aug 16, 2023	Federated Learning	—Unverified
DFedADMM: Dual Constraints Controlled Model Inconsistency for Decentralized Federated Learning	Aug 16, 2023	Federated Learning	—Unverified
NeFL: Nested Model Scaling for Federated Learning with System Heterogeneous Clients	Aug 15, 2023	Federated Learning	—Unverified

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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