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 626–650 of 6771 papers

Title	Date	Tasks	Status	Hype	Score
DBA: Distributed Backdoor Attacks against Federated Learning	May 1, 2020	Backdoor AttackFeature Importance	CodeCode Available	1	5
Decentralized Federated Learning: Balancing Communication and Computing Costs	Jul 26, 2021	Federated Learning	CodeCode Available	1	5
FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image Classification	Jun 28, 2022	Contrastive LearningFederated Learning	CodeCode Available	1	5
Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges	Nov 15, 2022	Federated Learning	CodeCode Available	1	5
FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction	Dec 3, 2022	Federated Learningmodel	CodeCode Available	1	5
Decentralized Federated Learning: A Segmented Gossip Approach	Aug 21, 2019	Federated Learning	CodeCode Available	1	5
Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition	Oct 26, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	1	5
FedSIS: Federated Split Learning with Intermediate Representation Sampling for Privacy-preserving Generalized Face Presentation Attack Detection	Aug 20, 2023	Domain GeneralizationFace Presentation Attack Detection	CodeCode Available	1	5
Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank Decomposition	Jun 28, 2024	Federated LearningGeneral Knowledge	CodeCode Available	1	5
FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy	Feb 21, 2023	Federated Learning	CodeCode Available	1	5
FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning	Dec 14, 2023	Computational EfficiencyFederated Learning	CodeCode Available	1	5
FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data Generation	Jan 11, 2024	AttributeDenoising	CodeCode Available	1	5
FedTADBench: Federated Time-Series Anomaly Detection Benchmark	Dec 19, 2022	Anomaly 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
Differentially private cross-silo federated learning	Jul 10, 2020	Federated Learning	CodeCode Available	1	5
FedZero: Leveraging Renewable Excess Energy in Federated Learning	May 24, 2023	Federated LearningScheduling	CodeCode Available	1	5
A Comprehensive View of Personalized Federated Learning on Heterogeneous Clinical Datasets	Sep 28, 2023	Federated LearningPersonalized Federated Learning	CodeCode Available	1	5
Adaptive and Parallel Split Federated Learning in Vehicular Edge Computing	May 29, 2024	Edge-computingFederated Learning	CodeCode Available	1	5
DESTRESS: Computation-Optimal and Communication-Efficient Decentralized Nonconvex Finite-Sum Optimization	Oct 4, 2021	Federated Learning	CodeCode Available	1	5
A Framework for Energy and Carbon Footprint Analysis of Distributed and Federated Edge Learning	Mar 18, 2021	Federated Learning	CodeCode Available	1	5
Asynchronous Federated Continual Learning	Apr 7, 2023	Continual LearningFederated 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
Asynchronous Federated Learning for Edge-assisted Vehicular Networks	Aug 3, 2022	Federated Learning	CodeCode Available	1	5
FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification	Jun 17, 2022	Federated LearningFew-Shot Learning	CodeCode Available	1	5
Analysis and Evaluation of Synchronous and Asynchronous FLchain	Dec 15, 2021	Federated Learning	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