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 3301–3325 of 6771 papers

Title	Date	Tasks	Status
FedBlockHealth: A Synergistic Approach to Privacy and Security in IoT-Enabled Healthcare through Federated Learning and Blockchain	Apr 16, 2023	Computational EfficiencyFederated Learning	—Unverified
FeDiSa: A Semi-asynchronous Federated Learning Framework for Power System Fault and Cyberattack Discrimination	Mar 28, 2023	Federated LearningIntrusion Detection	—Unverified
FedBlock: A Blockchain Approach to Federated Learning against Backdoor Attacks	Nov 5, 2024	backdoor defenseFederated Learning	—Unverified
Communication-Efficient Agnostic Federated Averaging	Apr 6, 2021	Federated LearningLanguage Modeling	—Unverified
A Reinforcement Learning-Based Approach to Graph Discovery in D2D-Enabled Federated Learning	Aug 7, 2023	Federated LearningReinforcement Learning (RL)	—Unverified
FedKBP: Federated dose prediction framework for knowledge-based planning in radiation therapy	Aug 17, 2024	Federated LearningModel Optimization	—Unverified
FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models	Oct 7, 2024	Federated Learning	—Unverified
FedKD: Communication Efficient Federated Learning via Knowledge Distillation	Aug 30, 2021	Federated LearningKnowledge Distillation	—Unverified
A Federated Approach to Few-Shot Hate Speech Detection for Marginalized Communities	Dec 6, 2024	Federated LearningFew-Shot Learning	—Unverified
A Convergence Theory for Federated Average: Beyond Smoothness	Nov 3, 2022	Edge-computingFederated Learning	—Unverified
FedMobile: Enabling Knowledge Contribution-aware Multi-modal Federated Learning with Incomplete Modalities	Feb 20, 2025	Federated LearningTransfer Learning	—Unverified
FedMPQ: Secure and Communication-Efficient Federated Learning with Multi-codebook Product Quantization	Apr 21, 2024	Federated LearningQuantization	—Unverified
Communication Efficient Adaptive Model-Driven Quantum Federated Learning	Jun 5, 2025	Federated Learning	—Unverified
FEDLAD: Federated Evaluation of Deep Leakage Attacks and Defenses	Nov 5, 2024	Federated LearningPrivacy Preserving	—Unverified
FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data	Sep 18, 2023	Federated LearningScheduling	—Unverified
Layer-wise and Dimension-wise Locally Adaptive Federated Learning	Oct 1, 2021	Federated Learning	—Unverified
Fed-LDR: Federated Local Data-infused Graph Creation with Node-centric Model Refinement	Nov 7, 2024	Federated Learning	—Unverified
FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated Learning with Bayesian Inference-Based Adaptive Dropout	Jul 14, 2023	Bayesian InferenceFederated Learning	—Unverified
Exploiting Label Skewness for Spiking Neural Networks in Federated Learning	Dec 23, 2024	Federated LearningKnowledge Distillation	—Unverified
FedLE: Federated Learning Client Selection with Lifespan Extension for Edge IoT Networks	Feb 14, 2023	Federated LearningPrivacy Preserving	—Unverified
A Real-time Contribution Measurement Method for Participants in Federated Learning	Sep 28, 2020	Federated Learning	—Unverified
Fed-BEV: A Federated Learning Framework for Modelling Energy Consumption of Battery Electric Vehicles	Aug 5, 2021	energy managementFederated Learning	—Unverified
FedBEns: One-Shot Federated Learning based on Bayesian Ensemble	Mar 19, 2025	Bayesian InferenceFederated Learning	—Unverified
FedLGA: Towards System-Heterogeneity of Federated Learning via Local Gradient Approximation	Dec 22, 2021	Federated Learning	—Unverified
Communication Efficiency Optimization of Federated Learning for Computing and Network Convergence of 6G Networks	Nov 28, 2023	Federated Learning	—Unverified

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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