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 3101–3125 of 6771 papers

Title	Date	Tasks	Status	Hype
Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex Hulls	Aug 14, 2023	Federated Learninggraph partitioning	CodeCode Available	0
Data-Efficient Energy-Aware Participant Selection for UAV-Enabled Federated Learning	Aug 14, 2023	Federated LearningSSIM	—Unverified	0
FedEdge AI-TC: A Semi-supervised Traffic Classification Method based on Trusted Federated Deep Learning for Mobile Edge Computing	Aug 14, 2023	Edge-computingFederated Learning	—Unverified	0
Approximate and Weighted Data Reconstruction Attack in Federated Learning	Aug 13, 2023	Bayesian OptimizationFederated Learning	—Unverified	0
Performance Analysis for Resource Constrained Decentralized Federated Learning Over Wireless Networks	Aug 12, 2023	Federated Learning	—Unverified	0
SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models	Aug 12, 2023	Federated Learningparameter-efficient fine-tuning	—Unverified	0
Towards Instance-adaptive Inference for Federated Learning	Aug 11, 2023	Federated Learningparameter-efficient fine-tuning	CodeCode Available	1
UFed-GAN: A Secure Federated Learning Framework with Constrained Computation and Unlabeled Data	Aug 10, 2023	Federated LearningGenerative Adversarial Network	—Unverified	0
FLShield: A Validation Based Federated Learning Framework to Defend Against Poisoning Attacks	Aug 10, 2023	Autonomous VehiclesFederated Learning	—Unverified	0
A Comprehensive Empirical Study of Bugs in Open-Source Federated Learning Frameworks	Aug 9, 2023	Federated Learning	—Unverified	0
Wirelessly Powered Federated Learning Networks: Joint Power Transfer, Data Sensing, Model Training, and Resource Allocation	Aug 9, 2023	Federated Learning	—Unverified	0
Tram-FL: Routing-based Model Training for Decentralized Federated Learning	Aug 9, 2023	Federated Learning	—Unverified	0
Feature Matching Data Synthesis for Non-IID Federated Learning	Aug 9, 2023	Data AugmentationFederated Learning	—Unverified	0
GIFD: A Generative Gradient Inversion Method with Feature Domain Optimization	Aug 9, 2023	Federated LearningImage Generation	CodeCode Available	1
Pelta: Shielding Transformers to Mitigate Evasion Attacks in Federated Learning	Aug 8, 2023	Adversarial AttackFederated Learning	—Unverified	0
Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate Gradients	Aug 8, 2023	Adversarial AttackFederated Learning	CodeCode Available	0
Backdoor Federated Learning by Poisoning Backdoor-Critical Layers	Aug 8, 2023	Backdoor AttackFederated Learning	—Unverified	0
ConDistFL: Conditional Distillation for Federated Learning from Partially Annotated Data	Aug 8, 2023	Federated LearningKnowledge Distillation	CodeCode Available	2
A Survey on Decentralized Federated Learning	Aug 8, 2023	Federated LearningPrivacy Preserving	—Unverified	0
FLIPS: Federated Learning using Intelligent Participant Selection	Aug 7, 2023	ClusteringFederated Learning	—Unverified	0
The Prospect of Enhancing Large-Scale Heterogeneous Federated Learning with Transformers	Aug 7, 2023	Federated Learning	—Unverified	0
A Reinforcement Learning-Based Approach to Graph Discovery in D2D-Enabled Federated Learning	Aug 7, 2023	Federated LearningReinforcement Learning (RL)	—Unverified	0
Binary Federated Learning with Client-Level Differential Privacy	Aug 7, 2023	Federated LearningPrivacy Preserving	—Unverified	0
The Copycat Perceptron: Smashing Barriers Through Collective Learning	Aug 7, 2023	Federated Learning	—Unverified	0
Cross-Silo Prototypical Calibration for Federated Learning with Non-IID Data	Aug 7, 2023	Contrastive LearningFederated Learning	CodeCode Available	1

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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