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 2251–2275 of 6771 papers

Title	Date	Tasks	Status
Adap DP-FL: Differentially Private Federated Learning with Adaptive Noise	Nov 29, 2022	Federated Learning	—Unverified
Concept Drift Detection in Federated Networked Systems	Sep 13, 2021	Dimensionality ReductionDrift Detection	—Unverified
Concept drift detection and adaptation for federated and continual learning	May 27, 2021	Continual LearningDrift Detection	—Unverified
Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning	May 10, 2024	Backdoor AttackData Poisoning	—Unverified
Compute-Update Federated Learning: A Lattice Coding Approach Over-the-Air	Sep 10, 2024	Federated Learning	—Unverified
A Survey on Efficient Federated Learning Methods for Foundation Model Training	Jan 9, 2024	Federated Learningparameter-efficient fine-tuning	—Unverified
A Federated Multi-View Deep Learning Framework for Privacy-Preserving Recommendations	Aug 25, 2020	Collaborative FilteringFederated Learning	—Unverified
Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approach	Jun 13, 2022	Deep Reinforcement LearningFederated Learning	—Unverified
Computation- and Communication-Efficient Online FL for Resource-Constrained Aerial Vehicles	Jun 3, 2025	Edge-computingFederated Learning	—Unverified
A Survey on Decentralized Federated Learning	Aug 8, 2023	Federated LearningPrivacy Preserving	—Unverified
Computation and Communication Efficient Lightweighting Vertical Federated Learning for Smart Building IoT	Mar 30, 2024	Computational EfficiencyFederated Learning	—Unverified
Adaptive Model Pruning and Personalization for Federated Learning over Wireless Networks	Sep 4, 2023	Federated Learning	—Unverified
A Federated Learning Scheme for Neuro-developmental Disorders: Multi-Aspect ASD Detection	Oct 31, 2022	Federated LearningPrivacy Preserving	—Unverified
AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity	Jan 22, 2024	Federated LearningGraph Learning	—Unverified
Accelerating Federated Learning via Momentum Gradient Descent	Oct 8, 2019	BIG-bench Machine LearningFederated Learning	—Unverified
Federated Learning on Stochastic Neural Networks	Jun 9, 2025	Edge-computingFederated Learning	—Unverified
Compression with Exact Error Distribution for Federated Learning	Oct 31, 2023	Federated Learning	—Unverified
Compression Boosts Differentially Private Federated Learning	Nov 10, 2020	Compressive SensingFederated Learning	—Unverified
A Survey on Cluster-based Federated Learning	Jan 29, 2025	Federated LearningPrivacy Preserving	—Unverified
Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data	Jun 16, 2022	Federated LearningQuantization	—Unverified
Compressed Bayesian Federated Learning for Reliable Passive Radio Sensing in Industrial IoT	May 9, 2024	Federated Learning	—Unverified
A Survey on Class Imbalance in Federated Learning	Mar 21, 2023	Federated LearningSurvey	—Unverified
A Federated Learning Platform as a Service for Advancing Stroke Management in European Clinical Centers	Oct 2, 2024	Decision MakingFederated Learning	—Unverified
Compressed and distributed least-squares regression: convergence rates with applications to Federated Learning	Aug 2, 2023	Federated Learningregression	—Unverified
A Survey on Blockchain-Based Federated Learning and Data Privacy	Jun 29, 2023	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