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 3901–3950 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
Toward Communication Efficient Adaptive Gradient Method	Sep 10, 2021	BIG-bench Machine LearningDistributed Optimization	—Unverified
Toward Data Heterogeneity of Federated Learning	Dec 17, 2022	Federated Learning	—Unverified
Toward Efficient Federated Learning in Multi-Channeled Mobile Edge Network with Layerd Gradient Compression	Sep 18, 2021	Federated Learning	—Unverified
Toward efficient resource utilization at edge nodes in federated learning	Sep 19, 2023	CPUFederated Learning	—Unverified
Toward Malicious Clients Detection in Federated Learning	May 14, 2025	Federated Learning	—Unverified
Towards Federated Graph Learning in One-shot Communication	Nov 18, 2024	Federated LearningGraph Learning	—Unverified
Toward Responsible Federated Large Language Models: Leveraging a Safety Filter and Constitutional AI	Feb 23, 2025	Federated Learning	—Unverified
Local and Central Differential Privacy for Robustness and Privacy in Federated Learning	Sep 8, 2020	Federated Learning	—Unverified
Towards Achieving Near-optimal Utility for Privacy-Preserving Federated Learning via Data Generation and Parameter Distortion	May 7, 2023	Federated LearningPrivacy Preserving	—Unverified
Towards Active Participant-Centric Vertical Federated Learning: Some Representations May Be All You Need	Oct 23, 2024	AllFederated Learning	—Unverified
Towards a Distributed Federated Learning Aggregation Placement using Particle Swarm Intelligence	Apr 22, 2025	Federated Learning	—Unverified
Towards a Federated Learning Framework for Heterogeneous Devices of Internet of Things	May 31, 2021	Federated Learning	—Unverified
Towards an Accountable and Reproducible Federated Learning: A FactSheets Approach	Feb 25, 2022	EthicsFederated Learning	—Unverified
Towards Artificial General or Personalized Intelligence? A Survey on Foundation Models for Personalized Federated Intelligence	May 11, 2025	Computational EfficiencyFederated Learning	—Unverified
Towards a Secure and Reliable Federated Learning using Blockchain	Jan 27, 2022	Federated Learning	—Unverified
Towards Bias Correction of FedAvg over Nonuniform and Time-Varying Communications	Jun 1, 2023	Federated Learning	—Unverified
Towards Bridging the FL Performance-Explainability Trade-Off: A Trustworthy 6G RAN Slicing Use-Case	Jul 24, 2023	Decision MakingExplainable Artificial Intelligence (XAI)	—Unverified
Towards Case-based Interpretability for Medical Federated Learning	Aug 24, 2024	Federated Learning	—Unverified
Towards Causal Federated Learning For Enhanced Robustness and Privacy	Apr 14, 2021	Federated LearningPrivacy Preserving	—Unverified
Towards Client Driven Federated Learning	May 24, 2024	Federated Learning	—Unverified
Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift	Jul 11, 2025	Collaborative FairnessFairness	—Unverified
Towards Collaborative Intelligence: Routability Estimation based on Decentralized Private Data	Mar 30, 2022	Federated Learning	—Unverified
Towards Communication Efficient and Fair Federated Personalized Sequential Recommendation	Aug 23, 2022	FairnessFederated Learning	—Unverified
Towards Communication-efficient Federated Learning via Sparse and Aligned Adaptive Optimization	May 28, 2024	Federated Learning	—Unverified
Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updates	Jul 29, 2022	Federated LearningVertical 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