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 2476–2500 of 6771 papers

Title	Date	Tasks	Status	Hype
Formal Logic Enabled Personalized Federated Learning Through Property Inference	Jan 15, 2024	Federated LearningFormal Logic	—Unverified	0
Quantum Privacy Aggregation of Teacher Ensembles (QPATE) for Privacy-preserving Quantum Machine Learning	Jan 15, 2024	Federated LearningPrivacy Preserving	—Unverified	0
Efficient Wireless Federated Learning via Low-Rank Gradient Factorization	Jan 15, 2024	Federated LearningLow-rank compression	CodeCode Available	0
FedRFQ: Prototype-Based Federated Learning with Reduced Redundancy, Minimal Failure, and Enhanced Quality	Jan 15, 2024	Federated Learning	—Unverified	0
The Effects of Data Imbalance Under a Federated Learning Approach for Credit Risk Forecasting	Jan 14, 2024	Federated LearningPrivacy Preserving	—Unverified	0
Gradient Coreset for Federated Learning	Jan 13, 2024	Federated Learning	CodeCode Available	0
FedDriveScore: Federated Scoring Driving Behavior with a Mixture of Metric Distributions	Jan 13, 2024	FairnessFederated Learning	—Unverified	0
Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models	Jan 12, 2024	Federated LearningPrivacy Preserving	—Unverified	0
Intelligent Data-Driven Architectural Features Orchestration for Network Slicing	Jan 12, 2024	Federated Learning	—Unverified	0
Decentralized Gossip Mutual Learning (GML) for automatic head and neck tumor segmentation	Jan 11, 2024	Federated LearningTumor Segmentation	—Unverified	0
FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data Generation	Jan 11, 2024	AttributeDenoising	CodeCode Available	1
Congruent Learning for Self-Regulated Federated Learning in 6G	Jan 10, 2024	Federated LearningManagement	CodeCode Available	0
Brave: Byzantine-Resilient and Privacy-Preserving Peer-to-Peer Federated Learning	Jan 10, 2024	Federated Learningimage-classification	—Unverified	0
Relaxed Contrastive Learning for Federated Learning	Jan 10, 2024	Contrastive LearningFederated Learning	CodeCode Available	1
Strategic Client Selection to Address Non-IIDness in HAPS-enabled FL Networks	Jan 10, 2024	AttributeDiversity	—Unverified	0
AdaFed: Fair Federated Learning via Adaptive Common Descent Direction	Jan 10, 2024	Federated Learning	—Unverified	0
Federated Unlearning: A Survey on Methods, Design Guidelines, and Evaluation Metrics	Jan 10, 2024	Federated Learning	CodeCode Available	1
Privacy-Preserving Sequential Recommendation with Collaborative Confusion	Jan 9, 2024	Collaborative FilteringFederated Learning	—Unverified	0
Deep Efficient Private Neighbor Generation for Subgraph Federated Learning	Jan 9, 2024	Federated LearningGraph Mining	—Unverified	0
A Survey on Efficient Federated Learning Methods for Foundation Model Training	Jan 9, 2024	Federated Learningparameter-efficient fine-tuning	—Unverified	0
Federated Analytics for 6G Networks: Applications, Challenges, and Opportunities	Jan 8, 2024	Distributed ComputingFederated Learning	—Unverified	0
Logits Poisoning Attack in Federated Distillation	Jan 8, 2024	Federated LearningKnowledge Distillation	—Unverified	0
GLOCALFAIR: Jointly Improving Global and Local Group Fairness in Federated Learning	Jan 7, 2024	FairnessFederated Learning	—Unverified	0
Multi-Modal Federated Learning for Cancer Staging over Non-IID Datasets with Unbalanced Modalities	Jan 7, 2024	Federated LearningMedical Image Analysis	CodeCode Available	0
Privacy-Preserving in Blockchain-based Federated Learning Systems	Jan 7, 2024	Federated LearningPrivacy Preserving	—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