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 4026–4050 of 6771 papers

Title	Date	Tasks	Status
Towards Verifiable Federated Learning	Feb 15, 2022	Federated Learning	—Unverified
Towards Zero-trust Security for the Metaverse	Feb 17, 2023	Federated Learning	—Unverified
Toward the Tradeoffs between Privacy, Fairness and Utility in Federated Learning	Nov 30, 2023	FairnessFederated Learning	—Unverified
Toward Understanding the Influence of Individual Clients in Federated Learning	Dec 20, 2020	Federated Learning	—Unverified
TPFL: A Trustworthy Personalized Federated Learning Framework via Subjective Logic	Oct 16, 2024	Decision MakingFederated Learning	—Unverified
Traceable Black-box Watermarks for Federated Learning	May 19, 2025	Federated Learning	—Unverified
Tracing Back the Malicious Clients in Poisoning Attacks to Federated Learning	Jul 9, 2024	Federated Learning	—Unverified
Trading Data For Learning: Incentive Mechanism For On-Device Federated Learning	Sep 11, 2020	Federated Learning	—Unverified
Trading-off Accuracy and Communication Cost in Federated Learning	Mar 18, 2025	Federated Learning	—Unverified
Trading Off Privacy, Utility and Efficiency in Federated Learning	Sep 1, 2022	Federated LearningVertical Federated Learning	—Unverified
TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized Federated Learning	Dec 16, 2024	Federated LearningScheduling	—Unverified
An Efficient Federated Learning Framework for Training Semantic Communication System	Oct 20, 2023	Federated LearningSemantic Communication	—Unverified
Training a Tokenizer for Free with Private Federated Learning	Mar 15, 2022	Federated LearningLanguage Modelling	—Unverified
Training Diffusion Models with Federated Learning	Jun 18, 2024	DenoisingFederated Learning	—Unverified
Training Keyword Spotting Models on Non-IID Data with Federated Learning	May 21, 2020	Data AugmentationFederated Learning	—Unverified
Training Large-Vocabulary Neural Language Models by Private Federated Learning for Resource-Constrained Devices	Jul 18, 2022	Federated LearningLanguage Modeling	—Unverified
Training Latency Minimization for Model-Splitting Allowed Federated Edge Learning	Jul 21, 2023	Edge-computingFederated Learning	—Unverified
Training Machine Learning models at the Edge: A Survey	Mar 5, 2024	Edge-computingFederated Learning	—Unverified
Training Mixed-Domain Translation Models via Federated Learning	May 3, 2022	BenchmarkingFederated Learning	—Unverified
Training on Fake Labels: Mitigating Label Leakage in Split Learning via Secure Dimension Transformation	Oct 11, 2024	Binary ClassificationFederated Learning	—Unverified
Training Production Language Models without Memorizing User Data	Sep 21, 2020	Federated LearningMemorization	—Unverified
Training Speech Recognition Models with Federated Learning: A Quality/Cost Framework	Oct 29, 2020	Federated Learningspeech-recognition	—Unverified
Training-time Neuron Alignment through Permutation Subspace for Improving Linear Mode Connectivity and Model Fusion	Feb 2, 2024	Federated LearningLinear Mode Connectivity	—Unverified
Tram-FL: Routing-based Model Training for Decentralized Federated Learning	Aug 9, 2023	Federated Learning	—Unverified
Transformer-based Federated Learning for Multi-Label Remote Sensing Image Classification	May 24, 2024	Federated Learningimage-classification	—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