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 1726–1750 of 6771 papers

Title	Date	Tasks	Status	Hype
Personalized federated learning based on feature fusion	Jun 24, 2024	Federated LearningPersonalized Federated Learning	—Unverified	0
Federated Transfer Learning Aided Interference Classification in GNSS Signals	Jun 23, 2024	ClassificationFederated Learning	—Unverified	0
Meta-FL: A Novel Meta-Learning Framework for Optimizing Heterogeneous Model Aggregation in Federated Learning	Jun 23, 2024	DiversityFederated Learning	—Unverified	0
Privacy Preserving Machine Learning for Electronic Health Records using Federated Learning and Differential Privacy	Jun 23, 2024	Federated LearningPrivacy Preserving	—Unverified	0
Semi-Variance Reduction for Fair Federated Learning	Jun 23, 2024	FairnessFederated Learning	—Unverified	0
Adaptive Digital Twin and Communication-Efficient Federated Learning Network Slicing for 5G-enabled Internet of Things	Jun 22, 2024	Decision MakingDemand Forecasting	—Unverified	0
Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in Federated Learning	Jun 22, 2024	Federated LearningInference Attack	—Unverified	0
Federated Adversarial Learning for Robust Autonomous Landing Runway Detection	Jun 22, 2024	Federated LearningLane Detection	—Unverified	0
Supersonic OT: Fast Unconditionally Secure Oblivious Transfer	Jun 21, 2024	Federated Learning	—Unverified	0
Privacy Preserved Blood Glucose Level Cross-Prediction: An Asynchronous Decentralized Federated Learning Approach	Jun 21, 2024	Federated LearningPrivacy Preserving	CodeCode Available	0
Embracing Federated Learning: Enabling Weak Client Participation via Partial Model Training	Jun 21, 2024	Federated Learning	—Unverified	0
Towards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approach	Jun 21, 2024	CPUDeep Reinforcement Learning	—Unverified	0
Rate-Splitting Multiple Access for Overloaded Multi-group Multicast: A First Experimental Study	Jun 21, 2024	FairnessFederated Learning	—Unverified	0
Safely Learning with Private Data: A Federated Learning Framework for Large Language Model	Jun 21, 2024	Federated LearningLanguage Modeling	CodeCode Available	1
Tempora-Fusion: Time-Lock Puzzle with Efficient Verifiable Homomorphic Linear Combination	Jun 21, 2024	Federated Learning	—Unverified	0
FLoCoRA: Federated learning compression with low-rank adaptation	Jun 20, 2024	Federated LearningModel Compression	CodeCode Available	0
Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated Learning	Jun 20, 2024	Federated LearningModel Poisoning	CodeCode Available	0
Communication-efficient Vertical Federated Learning via Compressed Error Feedback	Jun 20, 2024	Federated LearningVertical Federated Learning	CodeCode Available	0
Communication-Efficient Byzantine-Resilient Federated Zero-Order Optimization	Jun 20, 2024	Federated Learning	—Unverified	0
CollaFuse: Collaborative Diffusion Models	Jun 20, 2024	Edge-computingFederated Learning	CodeCode Available	0
DRACO: Decentralized Asynchronous Federated Learning over Row-Stochastic Wireless Networks	Jun 19, 2024	Federated Learning	—Unverified	0
Bayes' capacity as a measure for reconstruction attacks in federated learning	Jun 19, 2024	Federated LearningReconstruction Attack	—Unverified	0
A Resource-Adaptive Approach for Federated Learning under Resource-Constrained Environments	Jun 19, 2024	DiversityFairness	—Unverified	0
SFedCA: Credit Assignment-Based Active Client Selection Strategy for Spiking Federated Learning	Jun 18, 2024	Federated Learning	—Unverified	0
Security and Privacy of 6G Federated Learning-enabled Dynamic Spectrum Sharing	Jun 18, 2024	Federated Learning	—Unverified	0

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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