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 2601–2625 of 6771 papers

Title	Date	Tasks	Status
Federated Large Language Models: Current Progress and Future Directions	Sep 24, 2024	Federated LearningPrompt Learning	—Unverified
Federated Large Language Models: Feasibility, Robustness, Security and Future Directions	May 13, 2025	Federated LearningFew-Shot Learning	—Unverified
Federated Latent Class Regression for Hierarchical Data	Jun 22, 2022	Federated Learningregression	—Unverified
Federated Latent Factor Model for Bias-Aware Recommendation with Privacy-Preserving	Apr 21, 2025	Federated LearningPrivacy Preserving	—Unverified
Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes	Feb 6, 2024	Federated LearningModel Compression	—Unverified
Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications	Oct 8, 2023	Federated LearningSurvey	—Unverified
A Secure and Efficient Federated Learning Framework for NLP	Jan 28, 2022	Federated Learning	—Unverified
Federated Learning Algorithms for Generalized Mixed-effects Model (GLMM) on Horizontally Partitioned Data from Distributed Sources	Sep 28, 2021	Federated Learning	—Unverified
Federated Learning and AI Regulation in the European Union: Who is Responsible? -- An Interdisciplinary Analysis	Jul 11, 2024	Federated Learning	—Unverified
Federated Learning and Blockchain-enabled Fog-IoT Platform for Wearables in Predictive Healthcare	Jan 11, 2023	Federated LearningPrivacy Preserving	—Unverified
Federated Learning and catastrophic forgetting in pervasive computing: demonstration in HAR domain	Jul 17, 2022	Activity RecognitionFederated Learning	—Unverified
Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy	Jul 2, 2020	BIG-bench Machine LearningFederated Learning	—Unverified
Coordinated Replay Sample Selection for Continual Federated Learning	Oct 23, 2023	Continual LearningFederated Learning	—Unverified
Federated learning and next generation wireless communications: A survey on bidirectional relationship	Oct 14, 2021	BIG-bench Machine LearningDecision Making	—Unverified
Federated Learning and RAG Integration: A Scalable Approach for Medical Large Language Models	Dec 18, 2024	Federated LearningPrivacy Preserving	—Unverified
Federated Learning: A new frontier in the exploration of multi-institutional medical imaging data	Mar 25, 2025	Data IntegrationFederated Learning	—Unverified
Federated learning: Applications, challenges and future directions	May 18, 2022	Federated LearningMedical Diagnosis	—Unverified
Federated Learning Approach for Lifetime Prediction of Semiconductor Lasers	Mar 19, 2022	Federated LearningPrivacy Preserving	—Unverified
Federated Learning Approach to Mitigate Water Wastage	Aug 21, 2024	Federated LearningPrivacy Preserving	—Unverified
Federated Learning as a Mean-Field Game	Jul 8, 2021	Federated LearningPrivacy Preserving	—Unverified
Federated Learning as a Network Effects Game	Feb 16, 2023	Federated Learning	—Unverified
Federated Learning: A Signal Processing Perspective	Mar 31, 2021	BIG-bench Machine LearningFederated Learning	—Unverified
Federated Learning Assisted Distributed Energy Optimization	Nov 23, 2023	Decision MakingDistributed Optimization	—Unverified
FedCut: A Spectral Analysis Framework for Reliable Detection of Byzantine Colluders	Nov 24, 2022	Community DetectionFederated Learning	—Unverified
Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence	Feb 27, 2023	Federated LearningPrivacy Preserving	—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