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 6176–6200 of 6771 papers

Title	Date	Tasks	Status
(FL)^2: Overcoming Few Labels in Federated Semi-Supervised Learning	Oct 30, 2024	Federated Learning	CodeCode Available
STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networks	Nov 12, 2021	Federated Learning	CodeCode Available
Medical Federated Model with Mixture of Personalized and Sharing Components	Jun 26, 2023	Federated LearningTumor Segmentation	CodeCode Available
Deep Modeling and Optimization of Medical Image Classification	May 29, 2025	AvgClassification	CodeCode Available
Sparse Personalized Federated Learning	Jul 12, 2021	DiversityFederated Learning	CodeCode Available
Federated Learning for Time-Series Healthcare Sensing with Incomplete Modalities	May 20, 2024	Computational EfficiencyFederated Learning	CodeCode Available
Federated Learning with Heterogeneous Data Handling for Robust Vehicular Object Detection	May 2, 2024	Autonomous DrivingFederated Learning	CodeCode Available
Memory-adaptive Depth-wise Heterogenous Federated Learning	Mar 8, 2023	Federated Learning	CodeCode Available
Memory-aware curriculum federated learning for breast cancer classification	Jul 6, 2021	Breast Cancer DetectionCancer Classification	CodeCode Available
Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Framework	Jan 1, 2023	Federated Learning	CodeCode Available
DeepMediX: A Deep Learning-Driven Resource-Efficient Medical Diagnosis Across the Spectrum	Jul 1, 2023	Computational EfficiencyDiagnostic	CodeCode Available
Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning	Jun 9, 2021	Continual LearningFederated Learning	CodeCode Available
Federated Learning with a Single Shared Image	Jun 18, 2024	Federated LearningKnowledge Distillation	CodeCode Available
FLaPS: Federated Learning and Privately Scaling	Sep 13, 2020	Federated Learning	CodeCode Available
Personalized Federated Learning via Heterogeneous Modular Networks	Oct 26, 2022	Federated LearningPersonalized Federated Learning	CodeCode Available
Masked Random Noise for Communication Efficient Federated Learning	Aug 6, 2024	Federated Learning	CodeCode Available
An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models	Feb 11, 2025	Federated LearningPrivacy Preserving	CodeCode Available
Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated Learning	Nov 29, 2023	Federated LearningInference Attack	CodeCode Available
Federated Learning with Additional Mechanisms on Clients to Reduce Communication Costs	Aug 16, 2019	Federated Learning	CodeCode Available
Query-Efficient Adversarial Attack Against Vertical Federated Graph Learning	Nov 5, 2024	Adversarial AttackFederated Learning	CodeCode Available
Exploiting Unintended Feature Leakage in Collaborative Learning	May 10, 2018	Federated Learning	CodeCode Available
Federated Learning via Plurality Vote	Oct 6, 2021	Federated LearningQuantization	CodeCode Available
UniFed: A Universal Federation of a Mixture of Highly Heterogeneous Medical Image Classification Tasks	Jul 29, 2024	Federated Learningimage-classification	CodeCode Available
Explicit Personalization and Local Training: Double Communication Acceleration in Federated Learning	May 22, 2023	Federated Learning	CodeCode Available
Benchmarking Federated Learning for Semantic Datasets: Federated Scene Graph Generation	Dec 11, 2024	BenchmarkingFederated Learning	CodeCode Available

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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