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 2726–2750 of 6771 papers

Title	Date	Tasks	Status
AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning	Jul 16, 2021	Federated Learning	—Unverified
AGRAMPLIFIER: Defending Federated Learning Against Poisoning Attacks Through Local Update Amplification	Nov 13, 2023	Federated Learning	—Unverified
Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness	Jul 30, 2021	Distributed ComputingEdge-computing	—Unverified
Decentralized Blockchain-based Robust Multi-agent Multi-armed Bandit	Feb 6, 2024	Federated Learning	—Unverified
Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo	Jul 15, 2021	Federated Learning	—Unverified
Decentralized Bayesian Learning over Graphs	May 24, 2019	Bayesian InferenceFederated Learning	—Unverified
Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation	Mar 12, 2022	Federated LearningHyperparameter Optimization	—Unverified
Agnostic Personalized Federated Learning with Kernel Factorization	Sep 29, 2021	Federated LearningPersonalized Federated Learning	—Unverified
Adaptive Federated Learning and Digital Twin for Industrial Internet of Things	Oct 25, 2020	ClusteringDeep Reinforcement Learning	—Unverified
Decentralized and Model-Free Federated Learning: Consensus-Based Distillation in Function Space	Apr 1, 2021	Federated LearningKnowledge Distillation	—Unverified
Decentralised Traffic Incident Detection via Network Lasso	Feb 28, 2024	Federated Learning	—Unverified
AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving	Feb 17, 2023	Autonomous DrivingAutonomous Vehicles	—Unverified
Distributed Machine Learning with Sparse Heterogeneous Data	Dec 3, 2019	BIG-bench Machine LearningDenoising	—Unverified
Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation	Apr 20, 2021	Federated LearningImage Segmentation	—Unverified
Decentralised Resource Sharing in TinyML: Wireless Bilayer Gossip Parallel SGD for Collaborative Learning	Jan 8, 2025	Federated LearningPrivacy Preserving	—Unverified
Decentralised and collaborative machine learning framework for IoT	Dec 19, 2023	Federated LearningIncremental Learning	—Unverified
Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review	May 7, 2022	ArticlesFederated Learning	—Unverified
DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models	May 27, 2025	Federated Learning	—Unverified
AGIC: Approximate Gradient Inversion Attack on Federated Learning	Apr 28, 2022	Federated Learning	—Unverified
Decaf: Data Distribution Decompose Attack against Federated Learning	May 24, 2024	Federated Learning	—Unverified
Debiasing Federated Learning with Correlated Client Participation	Oct 2, 2024	Federated Learning	—Unverified
DearFSAC: An Approach to Optimizing Unreliable Federated Learning via Deep Reinforcement Learning	Jan 30, 2022	Deep Reinforcement LearningFederated Learning	—Unverified
A Unified Learn-to-Distort-Data Framework for Privacy-Utility Trade-off in Trustworthy Federated Learning	Jul 5, 2024	Federated LearningNavigate	—Unverified
Dealing Doubt: Unveiling Threat Models in Gradient Inversion Attacks under Federated Learning, A Survey and Taxonomy	May 16, 2024	Federated LearningPrivacy Preserving	—Unverified
DEAL: Decremental Energy-Aware Learning in a Federated System	Feb 5, 2021	energy managementFederated 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