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 2101–2125 of 6771 papers

Title	Date	Tasks	Status	Hype
Hybrid FedGraph: An efficient hybrid federated learning algorithm using graph convolutional neural network	Apr 15, 2024	Federated Learning	—Unverified	0
An Autoencoder-Based Constellation Design for AirComp in Wireless Federated Learning	Apr 15, 2024	Federated Learning	—Unverified	0
SpamDam: Towards Privacy-Preserving and Adversary-Resistant SMS Spam Detection	Apr 15, 2024	Adversarial RobustnessBackdoor Attack	CodeCode Available	0
VFLGAN: Vertical Federated Learning-based Generative Adversarial Network for Vertically Partitioned Data Publication	Apr 15, 2024	Federated LearningGenerative Adversarial Network	CodeCode Available	0
FedDistill: Global Model Distillation for Local Model De-Biasing in Non-IID Federated Learning	Apr 14, 2024	Federated Learningmodel	—Unverified	0
Bridging Data Islands: Geographic Heterogeneity-Aware Federated Learning for Collaborative Remote Sensing Semantic Segmentation	Apr 14, 2024	Earth ObservationFederated Learning	—Unverified	0
FedCCL: Federated Dual-Clustered Feature Contrast Under Domain Heterogeneity	Apr 14, 2024	ClusteringFederated Learning	—Unverified	0
MAP: Model Aggregation and Personalization in Federated Learning with Incomplete Classes	Apr 14, 2024	Federated Learning	—Unverified	0
PraFFL: A Preference-Aware Scheme in Fair Federated Learning	Apr 13, 2024	FairnessFederated Learning	CodeCode Available	0
FLoRA: Enhancing Vision-Language Models with Parameter-Efficient Federated Learning	Apr 12, 2024	Federated LearningImage Captioning	CodeCode Available	0
Federated Optimization with Doubly Regularized Drift Correction	Apr 12, 2024	Distributed OptimizationFederated Learning	—Unverified	0
Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing	Apr 12, 2024	Deep Reinforcement LearningEdge-computing	—Unverified	0
FedAuxHMTL: Federated Auxiliary Hard-Parameter Sharing Multi-Task Learning for Network Edge Traffic Classification	Apr 11, 2024	Federated LearningMulti-Task Learning	—Unverified	0
Bayesian Federated Model Compression for Communication and Computation Efficiency	Apr 11, 2024	Bayesian InferenceFederated Learning	—Unverified	0
Improving Multi-Center Generalizability of GAN-Based Fat Suppression using Federated Learning	Apr 10, 2024	Federated LearningGenerative Adversarial Network	—Unverified	0
The Sandwich meta-framework for architecture agnostic deep privacy-preserving transfer learning for non-invasive brainwave decoding	Apr 10, 2024	EEGEeg Decoding	—Unverified	0
Logit Calibration and Feature Contrast for Robust Federated Learning on Non-IID Data	Apr 10, 2024	Adversarial RobustnessFederated Learning	—Unverified	0
Collaborative Multi-source Domain Adaptation Through Optimal Transport	Apr 9, 2024	Domain AdaptationFederated Learning	—Unverified	0
Federated learning model for predicting major postoperative complications	Apr 9, 2024	Federated Learningmodel	—Unverified	0
Dynamic D2D-Assisted Federated Learning over O-RAN: Performance Analysis, MAC Scheduler, and Asymmetric User Selection	Apr 9, 2024	Federated Learning	—Unverified	0
FLEX: FLEXible Federated Learning Framework	Apr 9, 2024	Federated Learning	CodeCode Available	4
Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models	Apr 9, 2024	Federated Learningparameter-efficient fine-tuning	—Unverified	0
Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid Byzantines in Federated Learning	Apr 9, 2024	Federated LearningNetwork Pruning	CodeCode Available	0
Open-Source AI-based SE Tools: Opportunities and Challenges of Collaborative Software Learning	Apr 9, 2024	Federated Learning	—Unverified	0
pfl-research: simulation framework for accelerating research in Private Federated Learning	Apr 9, 2024	Federated Learning	CodeCode Available	3

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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