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 601–625 of 6771 papers

Title	Date	Tasks	Status	Hype	Score
Communication-Efficient Federated Learning with Binary Neural Networks	Oct 5, 2021	BinarizationFederated Learning	CodeCode Available	1	5
EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning	Aug 19, 2021	Federated Learning	CodeCode Available	1	5
Communication-Efficient Heterogeneous Federated Learning with Generalized Heavy-Ball Momentum	Nov 30, 2023	Federated Learning	CodeCode Available	1	5
Exploring Federated Unlearning: Review, Comparison, and Insights	Oct 30, 2023	Federated LearningPrivacy Preserving	CodeCode Available	1	5
Communication-Efficient Learning of Deep Networks from Decentralized Data	Feb 17, 2016	Federated LearningSpeech Recognition	CodeCode Available	1	5
FedJudge: Federated Legal Large Language Model	Sep 15, 2023	Continual LearningFederated Learning	CodeCode Available	1	5
Decentralized Federated Learning: Balancing Communication and Computing Costs	Jul 26, 2021	Federated Learning	CodeCode Available	1	5
EHRFL: Federated Learning Framework for Institution-Specific Model Construction using Electronic Health Records	Apr 20, 2024	Federated Learning	CodeCode Available	1	5
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning	Dec 3, 2018	Deep LearningFederated Learning	CodeCode Available	1	5
Asynchronous Federated Learning for Edge-assisted Vehicular Networks	Aug 3, 2022	Federated Learning	CodeCode Available	1	5
A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources	Mar 10, 2021	Federated Learning	CodeCode Available	1	5
FedLP: Layer-wise Pruning Mechanism for Communication-Computation Efficient Federated Learning	Mar 11, 2023	Federated Learning	CodeCode Available	1	5
Continual Local Training for Better Initialization of Federated Models	May 26, 2020	Federated Learning	CodeCode Available	1	5
FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter Sharing	Feb 13, 2024	Edge-computingFederated Learning	CodeCode Available	1	5
Federated Adaptive Prompt Tuning for Multi-Domain Collaborative Learning	Nov 15, 2022	Federated Learningimage-classification	CodeCode Available	1	5
FedMD: Heterogenous Federated Learning via Model Distillation	Oct 8, 2019	Federated LearningKnowledge Distillation	CodeCode Available	1	5
CRFL: Certifiably Robust Federated Learning against Backdoor Attacks	Jun 15, 2021	Federated Learning	CodeCode Available	1	5
Federated Learning Enables Big Data for Rare Cancer Boundary Detection	Apr 22, 2022	Boundary DetectionFederated Learning	CodeCode Available	1	5
Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks	Jan 17, 2023	Federated Learning	CodeCode Available	1	5
FedMix: Approximation of Mixup under Mean Augmented Federated Learning	Jul 1, 2021	Data AugmentationFederated Learning	CodeCode Available	1	5
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning	Jul 9, 2020	FairnessFederated Learning	CodeCode Available	1	5
DAGER: Exact Gradient Inversion for Large Language Models	May 24, 2024	DecoderFederated Learning	CodeCode Available	1	5
Data-Free Knowledge Distillation for Heterogeneous Federated Learning	May 20, 2021	Data-free Knowledge DistillationFederated Learning	CodeCode Available	1	5
Data Poisoning Attacks Against Federated Learning Systems	Jul 16, 2020	Data PoisoningFederated Learning	CodeCode Available	1	5
A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspective	Nov 27, 2023	Federated LearningPrivacy Preserving	CodeCode Available	1	5

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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