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 3526–3550 of 6771 papers

Title	Date	Tasks	Status
SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning	Jan 30, 2025	Federated LearningPersonalized Federated Learning	—Unverified
SaFL: Sybil-aware Federated Learning with Application to Face Recognition	Nov 7, 2023	Face RecognitionFederated Learning	—Unverified
SAGDA: Achieving O(ε^-2) Communication Complexity in Federated Min-Max Learning	Oct 2, 2022	Federated Learning	—Unverified
Sageflow: Robust Federated Learning against Both Stragglers and Adversaries	Dec 1, 2021	Federated Learning	—Unverified
SalientGrads: Sparse Models for Communication Efficient and Data Aware Distributed Federated Training	Apr 15, 2023	Federated Learning	—Unverified
Samplable Anonymous Aggregation for Private Federated Data Analysis	Jul 27, 2023	Federated Learning	—Unverified
Sample-based Federated Learning via Mini-batch SSCA	Mar 17, 2021	Federated LearningPrivacy Preserving	—Unverified
SAPAG: A Self-Adaptive Privacy Attack From Gradients	Sep 14, 2020	Federated LearningReconstruction Attack	—Unverified
Satellite Based Computing Networks with Federated Learning	Nov 20, 2021	Federated Learning	—Unverified
Satellite Federated Fine-Tuning for Foundation Models in Space Computing Power Networks	Apr 14, 2025	Federated Learning	—Unverified
SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Framework	Sep 20, 2024	Federated Learning	—Unverified
Meta-Computing Enhanced Federated Learning in IIoT: Satisfaction-Aware Incentive Scheme via DRL-Based Stackelberg Game	Feb 10, 2025	Deep Reinforcement LearningDistributed Computing	—Unverified
Scaff-PD: Communication Efficient Fair and Robust Federated Learning	Jul 25, 2023	FairnessFederated Learning	—Unverified
Scalable and Differentially Private Distributed Aggregation in the Shuffled Model	Jun 19, 2019	Federated LearningPrivacy Preserving	—Unverified
Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networking	May 25, 2022	Federated Learning	—Unverified
Scalable and Resource-Efficient Second-Order Federated Learning via Over-the-Air Aggregation	Oct 10, 2024	Federated Learning	—Unverified
Scalable Collaborative Learning via Representation Sharing	Nov 20, 2022	Federated LearningKnowledge Distillation	—Unverified
Scalable Federated Learning for Clients with Different Input Image Sizes and Numbers of Output Categories	Nov 15, 2023	Federated Learningimage-classification	—Unverified
Scalable Robust Federated Learning with Provable Security Guarantees	Sep 29, 2021	Federated Learning	—Unverified
Scalable Vertical Federated Learning via Data Augmentation and Amortized Inference	May 7, 2024	Bayesian InferenceData Augmentation	—Unverified
SCALA: Split Federated Learning with Concatenated Activations and Logit Adjustments	May 8, 2024	Federated Learning	—Unverified
SCALE: Self-regulated Clustered federAted LEarning in a Homogeneous Environment	Jul 25, 2024	Federated LearningPrivacy Preserving	—Unverified
Scaling Federated Learning for Fine-tuning of Large Language Models	Feb 1, 2021	Federated LearningSentiment Analysis	—Unverified
Scaling Language Model Size in Cross-Device Federated Learning	Mar 31, 2022	Federated LearningLanguage Modeling	—Unverified
Scaling Neuroscience Research using Federated Learning	Feb 16, 2021	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