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DFedADMM: Dual Constraints Controlled Model Inconsistency for Decentralized Federated Learning
To address the communication burden issues associated with federated learning (FL), decentralized federated learning (DFL) discards the central server and establishes a decentralized communication network, where each client communicates only with neighboring clients.
Visual Prompt Based Personalized Federated Learning
Then, the local model is trained on the input composed of raw data and a visual prompt to learn the distribution information contained in the prompt.
Subspace based Federated Unlearning
Federated learning (FL) enables multiple clients to train a machine learning model collaboratively without exchanging their local data.
FedHiSyn: A Hierarchical Synchronous Federated Learning Framework for Resource and Data Heterogeneity
As the efficiency of training in the ring topology prefers devices with homogeneous resources, the classification based on the computing capacity mitigates the impact of straggler effects.
Failure Prediction in Production Line Based on Federated Learning: An Empirical Study
Data protection across organizations is limiting the application of centralized learning (CL) techniques.
A Systematic Literature Review on Federated Learning: From A Model Quality Perspective
In this process, the server uses an incentive mechanism to encourage clients to contribute high-quality and large-volume data to improve the global model.
