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 3076–3100 of 6771 papers

Title	Date	Tasks	Status	Hype
Federated Learning Robust to Byzantine Attacks: Achieving Zero Optimality Gap	Aug 21, 2023	Federated Learning	—Unverified	0
Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges	Aug 21, 2023	Federated Learning	—Unverified	0
A Safe Deep Reinforcement Learning Approach for Energy Efficient Federated Learning in Wireless Communication Networks	Aug 21, 2023	Deep Reinforcement LearningFederated Learning	—Unverified	0
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
Federated Pseudo Modality Generation for Incomplete Multi-Modal MRI Reconstruction	Aug 20, 2023	Federated LearningMRI Reconstruction	—Unverified	0
GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning	Aug 20, 2023	FairnessFederated Learning	CodeCode Available	4
Preserving Specificity in Federated Graph Learning for fMRI-based Neurological Disorder Identification	Aug 20, 2023	Federated LearningFunctional Connectivity	—Unverified	0
Rethinking Client Drift in Federated Learning: A Logit Perspective	Aug 20, 2023	Federated Learning	—Unverified	0
Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated Learning	Aug 19, 2023	Federated Learning	CodeCode Available	1
Defending Label Inference Attacks in Split Learning under Regression Setting	Aug 18, 2023	Federated LearningPrivacy Preserving	—Unverified	0
Understanding the Role of Layer Normalization in Label-Skewed Federated Learning	Aug 18, 2023	Federated Learning	CodeCode Available	1
Towards Attack-tolerant Federated Learning via Critical Parameter Analysis	Aug 18, 2023	Federated Learning	CodeCode Available	1
Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Data	Aug 17, 2023	Federated Learning	—Unverified	0
Joint Power Control and Data Size Selection for Over-the-Air Computation Aided Federated Learning	Aug 17, 2023	Federated Learning	CodeCode Available	0
FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated Learning	Aug 17, 2023	Federated LearningPersonalized Federated Learning	CodeCode Available	1
Over-the-Air Computation Aided Federated Learning with the Aggregation of Normalized Gradient	Aug 17, 2023	Federated Learning	—Unverified	0
Controlling Federated Learning for Covertness	Aug 17, 2023	Federated Learning	—Unverified	0
APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service	Aug 17, 2023	Federated LearningPrivacy Preserving	CodeCode Available	1
DFedADMM: Dual Constraints Controlled Model Inconsistency for Decentralized Federated Learning	Aug 16, 2023	Federated Learning	—Unverified	0
Stochastic Controlled Averaging for Federated Learning with Communication Compression	Aug 16, 2023	Federated Learning	—Unverified	0
FedPop: Federated Population-based Hyperparameter Tuning	Aug 16, 2023	Computational EfficiencyEvolutionary Algorithms	CodeCode Available	1
NeFL: Nested Model Scaling for Federated Learning with System Heterogeneous Clients	Aug 15, 2023	Federated Learning	—Unverified	0
Fairness and Privacy in Federated Learning and Their Implications in Healthcare	Aug 15, 2023	FairnessFederated Learning	CodeCode Available	0
Aggregating Intrinsic Information to Enhance BCI Performance through Federated Learning	Aug 14, 2023	Brain Computer InterfaceEEG	CodeCode Available	0
DISBELIEVE: Distance Between Client Models is Very Essential for Effective Local Model Poisoning Attacks	Aug 14, 2023	Federated LearningMedical Image Analysis	—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