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 3901–3925 of 6771 papers

Title	Date	Tasks	Status
TiFL: A Tier-based Federated Learning System	Jan 25, 2020	Federated Learning	—Unverified
Tight Auditing of Differentially Private Machine Learning	Feb 15, 2023	Federated Learning	—Unverified
Tighter Generalization Bounds for Iterative Differentially Private Learning Algorithms	Jul 18, 2020	Federated LearningGeneralization Bounds	—Unverified
Tighter Performance Theory of FedExProx	Oct 20, 2024	Distributed OptimizationDiversity	—Unverified
Time-constrained Federated Learning (FL) in Push-Pull IoT Wireless Access	Nov 13, 2024	Federated LearningScheduling	—Unverified
Time-Correlated Sparsification for Communication-Efficient Federated Learning	Jan 21, 2021	Federated LearningQuantization	—Unverified
Time-Correlated Sparsification for Efficient Over-the-Air Model Aggregation in Wireless Federated Learning	Feb 17, 2022	Federated LearningModel Compression	—Unverified
Time-Distributed Backdoor Attacks on Federated Spiking Learning	Feb 5, 2024	Federated LearningSSIM	—Unverified
Timely Asynchronous Hierarchical Federated Learning: Age of Convergence	Jun 21, 2023	Federated Learning	—Unverified
Timely Communication in Federated Learning	Dec 31, 2020	Federated Learning	—Unverified
TimelyFL: Heterogeneity-aware Asynchronous Federated Learning with Adaptive Partial Training	Apr 14, 2023	Federated Learning	—Unverified
Time Minimization in Hierarchical Federated Learning	Oct 7, 2022	Federated Learning	—Unverified
Time-sensitive Learning for Heterogeneous Federated Edge Intelligence	Jan 26, 2023	Decision MakingEdge-computing	—Unverified
Time-triggered Federated Learning over Wireless Networks	Apr 26, 2022	Federated LearningPrivacy Preserving	—Unverified
TinyReptile: TinyML with Federated Meta-Learning	Apr 11, 2023	Federated LearningMeta-Learning	—Unverified
Maximizing Global Model Appeal in Federated Learning	May 30, 2022	Federated Learningmodel	—Unverified
TOFU: Towards Obfuscated Federated Updates by Encoding Weight Updates into Gradients from Proxy Data	Jan 21, 2022	Federated Learning	—Unverified
Tool-Aided Evolutionary LLM for Generative Policy Toward Efficient Resource Management in Wireless Federated Learning	May 16, 2025	Federated LearningLarge Language Model	—Unverified
Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey	Feb 6, 2023	Edge-computingFederated Learning	—Unverified
Topology-Based Reconstruction Prevention for Decentralised Learning	Dec 8, 2023	Federated LearningPrivacy Preserving	—Unverified
Topology Learning for Heterogeneous Decentralized Federated Learning over Unreliable D2D Networks	Dec 21, 2023	Federated Learning	—Unverified
TornadoAggregate: Accurate and Scalable Federated Learning via the Ring-Based Architecture	Dec 6, 2020	Federated Learning	—Unverified
To Talk or to Work: Delay Efficient Federated Learning over Mobile Edge Devices	Nov 1, 2021	Edge-computingFederated Learning	—Unverified
Energy Efficient Federated Learning over Heterogeneous Mobile Devices via Joint Design of Weight Quantization and Wireless Transmission	Dec 21, 2020	Edge-computingFederated Learning	—Unverified
To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices	Dec 22, 2020	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