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 4526–4550 of 6771 papers

Title	Date	Tasks	Status
Switch-Based Multi-Part Neural Network	Apr 25, 2025	Edge-computingFederated Learning	—Unverified
Sybil-based Virtual Data Poisoning Attacks in Federated Learning	May 15, 2025	Data PoisoningFederated Learning	—Unverified
SyncFed: Time-Aware Federated Learning through Explicit Timestamping and Synchronization	Jun 11, 2025	Federated Learning	—Unverified
Elastic Federated Learning over Open Radio Access Network (O-RAN) for Concurrent Execution of Multiple Distributed Learning Tasks	Apr 14, 2023	FairnessFederated Learning	—Unverified
Synergizing AI and Digital Twins for Next-Generation Network Optimization, Forecasting, and Security	Mar 8, 2025	Federated LearningReinforcement Learning (RL)	—Unverified
Synesthesia of Machines (SoM)-Aided FDD Precoding with Sensing Heterogeneity: A Vertical Federated Learning Approach	Jan 19, 2025	Federated LearningVertical Federated Learning	—Unverified
Synesthesia of Machines (SoM)-Aided Online FDD Precoding via Heterogeneous Multi-Modal Sensing: A Vertical Federated Learning Approach	Jun 9, 2025	Federated LearningVertical Federated Learning	—Unverified
Synthetic Data Aided Federated Learning Using Foundation Models	Jul 6, 2024	Data AugmentationFederated Learning	—Unverified
TabVFL: Improving Latent Representation in Vertical Federated Learning	Apr 27, 2024	Federated LearningRepresentation Learning	—Unverified
Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates	Feb 20, 2024	Federated Learningimage-classification	—Unverified
Tackling Computational Heterogeneity in FL: A Few Theoretical Insights	Jul 12, 2023	Federated Learning	—Unverified
Tackling Data Heterogeneity in Federated Time Series Forecasting	Nov 24, 2024	Federated LearningPrivacy Preserving	—Unverified
Tackling Dynamics in Federated Incremental Learning with Variational Embedding Rehearsal	Oct 19, 2021	Federated LearningIncremental Learning	—Unverified
Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation	Jul 23, 2024	Contrastive LearningFederated Learning	—Unverified
Tackling Heterogeneity in Medical Federated learning via Vision Transformers	Oct 13, 2023	Federated Learning	—Unverified
Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling	Dec 21, 2021	Federated Learning	—Unverified
Tackling the Non-IID Issue in Heterogeneous Federated Learning by Gradient Harmonization	Sep 13, 2023	Federated LearningPrivacy Preserving	—Unverified
Tailored Federated Learning: Leveraging Direction Regulation & Knowledge Distillation	Sep 29, 2024	Federated LearningKnowledge Distillation	—Unverified
Take History as a Mirror in Heterogeneous Federated Learning	Dec 16, 2023	Federated Learning	—Unverified
Take Your Pick: Enabling Effective Personalized Federated Learning within Low-dimensional Feature Space	Jul 26, 2023	Federated LearningPersonalized Federated Learning	—Unverified
Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domains	Mar 14, 2024	ClusteringFederated Learning	—Unverified
Taming Fat-Tailed ("Heavier-Tailed'' with Potentially Infinite Variance) Noise in Federated Learning	Oct 3, 2022	Federated Learning	—Unverified
Taming Gradient Variance in Federated Learning with Networked Control Variates	Oct 26, 2023	Federated Learning	—Unverified
TAMUNA: Doubly Accelerated Distributed Optimization with Local Training, Compression, and Partial Participation	Feb 20, 2023	Distributed OptimizationFederated Learning	—Unverified
TAPFed: Threshold Secure Aggregation for Privacy-Preserving Federated Learning	Jan 9, 2025	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