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 301–325 of 6771 papers

Title	Date	Tasks	Status	Hype
Communication Efficient Federated Learning for Multilingual Neural Machine Translation with Adapter	May 21, 2023	ClusteringFederated Learning	CodeCode Available	1
Communication-Efficient Federated Learning through Adaptive Weight Clustering and Server-Side Distillation	Jan 25, 2024	ClusteringFederated Learning	CodeCode Available	1
Communication-Efficient Federated Learning with Binary Neural Networks	Oct 5, 2021	BinarizationFederated Learning	CodeCode Available	1
Communication-Efficient Heterogeneous Federated Learning with Generalized Heavy-Ball Momentum	Nov 30, 2023	Federated Learning	CodeCode Available	1
Bias Propagation in Federated Learning	Sep 5, 2023	FairnessFederated Learning	CodeCode Available	1
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
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning	Aug 23, 2021	Federated LearningMisconceptions	CodeCode Available	1
ARFED: Attack-Resistant Federated averaging based on outlier elimination	Nov 8, 2021	Data PoisoningFederated Learning	CodeCode Available	1
CRFL: Certifiably Robust Federated Learning against Backdoor Attacks	Jun 15, 2021	Federated Learning	CodeCode Available	1
Federated Adaptive Prompt Tuning for Multi-Domain Collaborative Learning	Nov 15, 2022	Federated Learningimage-classification	CodeCode Available	1
Omnidirectional Transfer for Quasilinear Lifelong Learning	Apr 27, 2020	Federated LearningLifelong learning	CodeCode Available	1
DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices	Dec 8, 2024	Edge-computingFederated Learning	CodeCode Available	1
TabLeak: Tabular Data Leakage in Federated Learning	Oct 4, 2022	Federated LearningReconstruction Attack	CodeCode Available	1
Data Poisoning Attacks Against Federated Learning Systems	Jul 16, 2020	Data PoisoningFederated Learning	CodeCode Available	1
Decentralized Federated Learning: Balancing Communication and Computing Costs	Jul 26, 2021	Federated Learning	CodeCode Available	1
Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges	Nov 15, 2022	Federated Learning	CodeCode Available	1
Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank Decomposition	Jun 28, 2024	Federated LearningGeneral Knowledge	CodeCode Available	1
Deep Federated Learning for Autonomous Driving	Oct 12, 2021	Autonomous DrivingFederated Learning	CodeCode Available	1
FedAdapter: Efficient Federated Learning for Modern NLP	May 20, 2022	Federated Learning	CodeCode Available	1
Differentially Private Federated Learning on Heterogeneous Data	Nov 17, 2021	Federated Learning	CodeCode Available	1
Adaptive and Parallel Split Federated Learning in Vehicular Edge Computing	May 29, 2024	Edge-computingFederated Learning	CodeCode Available	1
A Framework for Energy and Carbon Footprint Analysis of Distributed and Federated Edge Learning	Mar 18, 2021	Federated Learning	CodeCode Available	1
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training	Jun 1, 2022	Federated LearningPersonalized Federated Learning	CodeCode Available	1
Distantly Supervised Relation Extraction in Federated Settings	Aug 12, 2020	DenoisingFederated Learning	CodeCode Available	1
Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage	Mar 29, 2022	Bayesian OptimizationFederated 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