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 2726–2750 of 6771 papers

Title	Date	Tasks	Status	Hype
A Quality-of-Service Compliance System using Federated Learning and Optimistic Rollups	Nov 14, 2023	Edge-computingFederated Learning	—Unverified	0
Federated Skewed Label Learning with Logits Fusion	Nov 14, 2023	Federated Learning	—Unverified	0
Leveraging Foundation Models to Improve Lightweight Clients in Federated Learning	Nov 14, 2023	Federated Learning	—Unverified	0
The Impact of Adversarial Node Placement in Decentralized Federated Learning Networks	Nov 14, 2023	Federated Learning	CodeCode Available	0
EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning	Nov 13, 2023	Federated Learning	—Unverified	0
A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks	Nov 13, 2023	class-incremental learningClass Incremental Learning	—Unverified	0
FedOpenHAR: Federated Multi-Task Transfer Learning for Sensor-Based Human Activity Recognition	Nov 13, 2023	Activity RecognitionFederated Learning	—Unverified	0
Data-Aware Gradient Compression for FL in Communication-Constrained Mobile Computing	Nov 13, 2023	Federated Learning	—Unverified	0
AGRAMPLIFIER: Defending Federated Learning Against Poisoning Attacks Through Local Update Amplification	Nov 13, 2023	Federated Learning	—Unverified	0
Tunable Soft Prompts are Messengers in Federated Learning	Nov 12, 2023	Federated LearningLanguage Modelling	—Unverified	0
pFedES: Model Heterogeneous Personalized Federated Learning with Feature Extractor Sharing	Nov 12, 2023	Federated LearningPersonalized Federated Learning	—Unverified	0
Concept Matching: Clustering-based Federated Continual Learning	Nov 12, 2023	ClusteringContinual Learning	—Unverified	0
Resource-Aware Hierarchical Federated Learning for Video Caching in Wireless Networks	Nov 12, 2023	CPUFederated Learning	—Unverified	0
A Comprehensive Survey On Client Selections in Federated Learning	Nov 12, 2023	Federated LearningSurvey	—Unverified	0
Personalized Federated Learning via ADMM with Moreau Envelope	Nov 12, 2023	Federated LearningPersonalized Federated Learning	—Unverified	0
FLASH-RL: Federated Learning Addressing System and Static Heterogeneity using Reinforcement Learning	Nov 12, 2023	Federated Learningreinforcement-learning	CodeCode Available	1
Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark	Nov 12, 2023	FairnessFederated Learning	CodeCode Available	1
Privacy Risks Analysis and Mitigation in Federated Learning for Medical Images	Nov 11, 2023	Federated Learning	CodeCode Available	0
IODeep: an IOD for the introduction of deep learning in the DICOM standard	Nov 10, 2023	DiagnosticFederated Learning	CodeCode Available	0
Blockchain-Enabled Federated Learning Approach for Vehicular Networks	Nov 10, 2023	Federated Learning	—Unverified	0
Aggregation Weighting of Federated Learning via Generalization Bound Estimation	Nov 10, 2023	Federated LearningGeneralization Bounds	—Unverified	0
Federated Learning with Manifold Regularization and Normalized Update Reaggregation	Nov 10, 2023	Federated Learning	—Unverified	0
Scale-MIA: A Scalable Model Inversion Attack against Secure Federated Learning via Latent Space Reconstruction	Nov 10, 2023	Federated Learning	CodeCode Available	0
Federated Learning Across Decentralized and Unshared Archives for Remote Sensing Image Classification	Nov 10, 2023	Federated Learningimage-classification	—Unverified	0
Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency	Nov 9, 2023	Deep LearningFederated 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