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 2201–2225 of 6771 papers

Title	Date	Tasks	Status
DWFL: Enhancing Federated Learning through Dynamic Weighted Averaging	Nov 7, 2024	Federated LearningPrivacy Preserving	—Unverified
DVFL: A Vertical Federated Learning Method for Dynamic Data	Nov 5, 2021	Federated LearningKnowledge Distillation	—Unverified
DU-Shapley: A Shapley Value Proxy for Efficient Dataset Valuation	Jun 3, 2023	Federated Learning	—Unverified
BN-SCAFFOLD: controlling the drift of Batch Normalization statistics in Federated Learning	Oct 4, 2024	Federated Learning	—Unverified
Blood Glucose Level Prediction: A Graph-based Explainable Method with Federated Learning	Dec 19, 2023	AttributeFederated Learning	—Unverified
Addressing Heterogeneity in Federated Learning: Challenges and Solutions for a Shared Production Environment	Aug 18, 2024	Federated Learning	—Unverified
A Blockchain Solution for Collaborative Machine Learning over IoT	Nov 23, 2023	Federated LearningIncremental Learning	—Unverified
Dubhe: Towards Data Unbiasedness with Homomorphic Encryption in Federated Learning Client Selection	Sep 8, 2021	Federated Learning	—Unverified
Dual-Segment Clustering Strategy for Hierarchical Federated Learning in Heterogeneous Wireless Environments	May 15, 2024	ClusteringFederated Learning	—Unverified
BlockFLow: An Accountable and Privacy-Preserving Solution for Federated Learning	Jul 8, 2020	Federated LearningPrivacy Preserving	—Unverified
Learning to Prompt Your Domain for Vision-Language Models	Oct 4, 2023	Domain AdaptationFederated Learning	—Unverified
BlockFLA: Accountable Federated Learning via Hybrid Blockchain Architecture	Oct 14, 2020	Federated Learning	—Unverified
Dual Model Replacement:invisible Multi-target Backdoor Attack based on Federal Learning	Apr 22, 2024	Backdoor AttackData Poisoning	—Unverified
DualGFL: Federated Learning with a Dual-Level Coalition-Auction Game	Dec 20, 2024	AttributeFederated Learning	—Unverified
DualFL: A Duality-based Federated Learning Algorithm with Communication Acceleration in the General Convex Regime	May 17, 2023	Computational EfficiencyDistributed Optimization	—Unverified
Block-FeDL: Electric Vehicle Charging Load Forecasting using Federated Learning and Blockchain	May 29, 2024	Federated LearningLoad Forecasting	—Unverified
An Efficient and Robust System for Vertically Federated Random Forest	Jan 26, 2022	Federated Learning	—Unverified
Blockchain-enabled Trustworthy Federated Unlearning	Jan 29, 2024	Federated Learning	—Unverified
Dual-Criterion Model Aggregation in Federated Learning: Balancing Data Quantity and Quality	Nov 12, 2024	Federated LearningPrivacy Preserving	—Unverified
dsLassoCov: a federated machine learning approach incorporating covariate control	Dec 11, 2024	Federated LearningPrivacy Preserving	—Unverified
An Efficient and Multi-private Key Secure Aggregation for Federated Learning	Jun 15, 2023	Federated Learning	—Unverified
Blockchain-Enabled Privacy-Preserving Second-Order Federated Edge Learning in Personalized Healthcare	May 31, 2025	Federated LearningPrivacy Preserving	—Unverified
Blockchain-Enabled Federated Learning: A Reference Architecture Design, Implementation, and Verification	Jun 19, 2023	Federated Learning	—Unverified
An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee	Nov 3, 2020	Distributed ComputingFairness	—Unverified
Addressing Data Heterogeneity in Federated Learning with Adaptive Normalization-Free Feature Recalibration	Oct 2, 2024	Federated LearningPersonalized 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