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 3051–3075 of 6771 papers

Title	Date	Tasks	Status
Federated Nonparametric Hypothesis Testing with Differential Privacy Constraints: Optimal Rates and Adaptive Tests	Jun 10, 2024	Federated Learning	—Unverified
Federated Object Detection for Quality Inspection in Shared Production	Jun 30, 2023	Federated LearningObject	—Unverified
Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices	Feb 8, 2024	Federated LearningOffline RL	—Unverified
Federated One-Shot Learning with Data Privacy and Objective-Hiding	Apr 29, 2025	Federated LearningInformation Retrieval	—Unverified
A Review of Privacy-preserving Federated Learning for the Internet-of-Things	Apr 24, 2020	BIG-bench Machine LearningFederated Learning	—Unverified
Federated Learning for Heterogeneous Bandits with Unobserved Contexts	Mar 29, 2023	Federated LearningMulti-Armed Bandits	—Unverified
Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Models	Oct 16, 2024	DecoderFederated Learning	—Unverified
Delay Optimization of a Federated Learning-based UAV-aided IoT network	Feb 10, 2025	Federated Learning	—Unverified
Federated Optimization of Smooth Loss Functions	Jan 6, 2022	Federated Learning	—Unverified
Federated Optimization with Doubly Regularized Drift Correction	Apr 12, 2024	Distributed OptimizationFederated Learning	—Unverified
Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learning	Sep 3, 2023	Continual LearningFederated Learning	—Unverified
Delay-Tolerant Local SGD for Efficient Distributed Training	Jan 1, 2021	Federated Learning	—Unverified
FedCanon: Non-Convex Composite Federated Learning with Efficient Proximal Operation on Heterogeneous Data	Apr 16, 2025	Computational EfficiencyFederated Learning	—Unverified
Probably Approximately Correct Federated Learning	Apr 10, 2023	Federated LearningPAC learning	—Unverified
Federated PCA and Estimation for Spiked Covariance Matrices: Optimal Rates and Efficient Algorithm	Nov 23, 2024	Federated Learning	—Unverified
Federated prediction for scalable and privacy-preserved knowledge-based planning in radiotherapy	May 20, 2025	Federated Learning	—Unverified
A review of federated learning in renewable energy applications: Potential, challenges, and future directions	Dec 18, 2023	Federated LearningPrivacy Preserving	—Unverified
Federated Privacy-preserving Collaborative Filtering for On-Device Next App Prediction	Feb 5, 2023	Collaborative FilteringFederated Learning	—Unverified
Federated Prompt-based Decision Transformer for Customized VR Services in Mobile Edge Computing System	Feb 15, 2024	Edge-computingFederated Learning	—Unverified
Democratizing AI in Africa: FL for Low-Resource Edge Devices	Aug 30, 2024	Federated Learning	—Unverified
FedCALM: Conflict-aware Layer-wise Mitigation for Selective Aggregation in Deeper Personalized Federated Learning	Jan 1, 2025	Federated LearningPersonalized Federated Learning	—Unverified
FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler	May 24, 2024	Federated Learning	—Unverified
Federated Quantum Long Short-term Memory (FedQLSTM)	Dec 21, 2023	Federated LearningQuantum Machine Learning	—Unverified
Federated Quantum Machine Learning	Mar 22, 2021	BIG-bench Machine LearningFederated Learning	—Unverified
A Crowdsourcing Framework for On-Device Federated Learning	Nov 4, 2019	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