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 201–225 of 6771 papers

Title	Date	Tasks	Status	Hype
FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning	Dec 14, 2023	Computational EfficiencyFederated Learning	CodeCode Available	1
CLIP-guided Federated Learning on Heterogeneous and Long-Tailed Data	Dec 14, 2023	Contrastive LearningFederated Learning	CodeCode Available	1
Language-Guided Transformer for Federated Multi-Label Classification	Dec 12, 2023	ClassificationFederated Learning	CodeCode Available	1
Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes	Dec 11, 2023	Federated LearningLarge Language Model	CodeCode Available	1
Exploiting Label Skews in Federated Learning with Model Concatenation	Dec 11, 2023	Federated Learningimage-classification	CodeCode Available	1
Point Transformer with Federated Learning for Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained Whole Slide Images	Dec 11, 2023	Federated Learningwhole slide images	CodeCode Available	1
Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts	Dec 5, 2023	Active LearningDiversity	CodeCode Available	1
Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents	Dec 3, 2023	Dataset DistillationFederated Learning	CodeCode Available	1
Communication-Efficient Heterogeneous Federated Learning with Generalized Heavy-Ball Momentum	Nov 30, 2023	Federated Learning	CodeCode Available	1
Federated Learning with Diffusion Models for Privacy-Sensitive Vision Tasks	Nov 28, 2023	Federated LearningImage Generation	CodeCode Available	1
FedECA: A Federated External Control Arm Method for Causal Inference with Time-To-Event Data in Distributed Settings	Nov 28, 2023	Causal InferenceFederated Learning	CodeCode Available	1
A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspective	Nov 27, 2023	Federated LearningPrivacy Preserving	CodeCode Available	1
VeryFL: A Verify Federated Learning Framework Embedded with Blockchain	Nov 27, 2023	Federated Learning	CodeCode Available	1
FedHCA^2: Towards Hetero-Client Federated Multi-Task Learning	Nov 22, 2023	DecoderFederated Learning	CodeCode Available	1
FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients	Nov 19, 2023	Federated Learning	CodeCode Available	1
FedFusion: Manifold Driven Federated Learning for Multi-satellite and Multi-modality Fusion	Nov 16, 2023	Edge-computingFederated Learning	CodeCode Available	1
Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark	Nov 12, 2023	FairnessFederated Learning	CodeCode Available	1
FLASH-RL: Federated Learning Addressing System and Static Heterogeneity using Reinforcement Learning	Nov 12, 2023	Federated Learningreinforcement-learning	CodeCode Available	1
Data Valuation and Detections in Federated Learning	Nov 9, 2023	Data ValuationFederated Learning	CodeCode Available	1
Exploring Federated Unlearning: Review, Comparison, and Insights	Oct 30, 2023	Federated LearningPrivacy Preserving	CodeCode Available	1
Adaptive Test-Time Personalization for Federated Learning	Oct 28, 2023	Federated LearningPersonalized Federated Learning	CodeCode Available	1
Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated Learning	Oct 27, 2023	Federated LearningPersonalized Federated Learning	CodeCode Available	1
Navigating Data Heterogeneity in Federated Learning A Semi-Supervised Federated Object Detection	Oct 26, 2023	Autonomous DrivingFederated Learning	CodeCode Available	1
Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization	Oct 23, 2023	Federated Learning	CodeCode Available	1
Serverless Federated Learning with flwr-serverless	Oct 23, 2023	Federated Learning	CodeCode Available	1

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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