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 3351–3400 of 6771 papers

Title	Date	Tasks	Status
Federated Learning for Sparse Principal Component Analysis	Nov 15, 2023	Computational EfficiencyFederated Learning	—Unverified
Exploring the Privacy-Energy Consumption Tradeoff for Split Federated Learning	Nov 15, 2023	Federated Learning	—Unverified
Federated Skewed Label Learning with Logits Fusion	Nov 14, 2023	Federated Learning	—Unverified
A Quality-of-Service Compliance System using Federated Learning and Optimistic Rollups	Nov 14, 2023	Edge-computingFederated Learning	—Unverified
The Impact of Adversarial Node Placement in Decentralized Federated Learning Networks	Nov 14, 2023	Federated Learning	CodeCode Available
Leveraging Foundation Models to Improve Lightweight Clients in Federated Learning	Nov 14, 2023	Federated Learning	—Unverified
A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks	Nov 13, 2023	class-incremental learningClass Incremental Learning	—Unverified
Data-Aware Gradient Compression for FL in Communication-Constrained Mobile Computing	Nov 13, 2023	Federated Learning	—Unverified
EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning	Nov 13, 2023	Federated Learning	—Unverified
FedOpenHAR: Federated Multi-Task Transfer Learning for Sensor-Based Human Activity Recognition	Nov 13, 2023	Activity RecognitionFederated Learning	—Unverified
AGRAMPLIFIER: Defending Federated Learning Against Poisoning Attacks Through Local Update Amplification	Nov 13, 2023	Federated Learning	—Unverified
Tunable Soft Prompts are Messengers in Federated Learning	Nov 12, 2023	Federated LearningLanguage Modelling	—Unverified
A Comprehensive Survey On Client Selections in Federated Learning	Nov 12, 2023	Federated LearningSurvey	—Unverified
Resource-Aware Hierarchical Federated Learning for Video Caching in Wireless Networks	Nov 12, 2023	CPUFederated Learning	—Unverified
Concept Matching: Clustering-based Federated Continual Learning	Nov 12, 2023	ClusteringContinual Learning	—Unverified
Personalized Federated Learning via ADMM with Moreau Envelope	Nov 12, 2023	Federated LearningPersonalized Federated Learning	—Unverified
pFedES: Model Heterogeneous Personalized Federated Learning with Feature Extractor Sharing	Nov 12, 2023	Federated LearningPersonalized Federated Learning	—Unverified
Privacy Risks Analysis and Mitigation in Federated Learning for Medical Images	Nov 11, 2023	Federated Learning	CodeCode Available
Federated Learning with Manifold Regularization and Normalized Update Reaggregation	Nov 10, 2023	Federated Learning	—Unverified
Aggregation Weighting of Federated Learning via Generalization Bound Estimation	Nov 10, 2023	Federated LearningGeneralization Bounds	—Unverified
IODeep: an IOD for the introduction of deep learning in the DICOM standard	Nov 10, 2023	DiagnosticFederated Learning	CodeCode Available
Scale-MIA: A Scalable Model Inversion Attack against Secure Federated Learning via Latent Space Reconstruction	Nov 10, 2023	Federated Learning	CodeCode Available
Federated Learning Across Decentralized and Unshared Archives for Remote Sensing Image Classification	Nov 10, 2023	Federated Learningimage-classification	—Unverified
Blockchain-Enabled Federated Learning Approach for Vehicular Networks	Nov 10, 2023	Federated Learning	—Unverified
Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency	Nov 9, 2023	Deep LearningFederated Learning	—Unverified
Personalized Online Federated Learning with Multiple Kernels	Nov 9, 2023	Federated Learning	CodeCode Available
The Paradox of Noise: An Empirical Study of Noise-Infusion Mechanisms to Improve Generalization, Stability, and Privacy in Federated Learning	Nov 9, 2023	Federated LearningPrivacy Preserving	—Unverified
Accurate Autism Spectrum Disorder prediction using Support Vector Classifier based on Federated Learning (SVCFL)	Nov 8, 2023	DiagnosticFederated Learning	—Unverified
Robust and Communication-Efficient Federated Domain Adaptation via Random Features	Nov 8, 2023	Domain AdaptationFederated Learning	CodeCode Available
Decentralized Personalized Online Federated Learning	Nov 8, 2023	Federated Learning	—Unverified
Cross-Silo Federated Learning Across Divergent Domains with Iterative Parameter Alignment	Nov 8, 2023	FairnessFederated Learning	CodeCode Available
Edge-assisted U-Shaped Split Federated Learning with Privacy-preserving for Internet of Things	Nov 8, 2023	Federated LearningPrivacy Preserving	—Unverified
Asynchronous Message-Passing and Zeroth-Order Optimization Based Distributed Learning with a Use-Case in Resource Allocation in Communication Networks	Nov 8, 2023	Distributed OptimizationFederated Learning	—Unverified
SaFL: Sybil-aware Federated Learning with Application to Face Recognition	Nov 7, 2023	Face RecognitionFederated Learning	—Unverified
Input Reconstruction Attack against Vertical Federated Large Language Models	Nov 7, 2023	Federated LearningGPU	—Unverified
Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networks	Nov 7, 2023	Federated Learning	—Unverified
Blind Federated Learning via Over-the-Air q-QAM	Nov 7, 2023	Federated Learning	—Unverified
Convergence Analysis of Sequential Federated Learning on Heterogeneous Data	Nov 6, 2023	Federated Learning	CodeCode Available
CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centers	Nov 6, 2023	Federated Learning	—Unverified
Asynchronous Local Computations in Distributed Bayesian Learning	Nov 6, 2023	Federated Learning	—Unverified
Goal-Oriented Wireless Communication Resource Allocation for Cyber-Physical Systems	Nov 6, 2023	Decision MakingDistributed Optimization	—Unverified
Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches	Nov 6, 2023	Federated LearningPrivacy Preserving	—Unverified
Communication Efficient and Privacy-Preserving Federated Learning Based on Evolution Strategies	Nov 5, 2023	Federated LearningPrivacy Preserving	CodeCode Available
Hybrid quantum image classification and federated learning for hepatic steatosis diagnosis	Nov 4, 2023	DiagnosticFederated Learning	—Unverified
Sample Complexity of Linear Regression Models for Opinion Formation in Networks	Nov 4, 2023	Federated Learningregression	CodeCode Available
Epidemic Decision-making System Based Federated Reinforcement Learning	Nov 3, 2023	Decision MakingFederated Learning	—Unverified
Client Orchestration and Cost-Efficient Joint Optimization for NOMA-Enabled Hierarchical Federated Learning	Nov 3, 2023	Federated LearningProblem Decomposition	—Unverified
Heterogeneous federated collaborative filtering using FAIR: Federated Averaging in Random Subspaces	Nov 3, 2023	Collaborative FilteringFederated Learning	CodeCode Available
Dynamic Fair Federated Learning Based on Reinforcement Learning	Nov 2, 2023	FairnessFederated Learning	—Unverified
Decentralized Federated Learning on the Edge over Wireless Mesh Networks	Nov 2, 2023	BenchmarkingFederated Learning	—Unverified

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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