A systematic literature review on federated learning: From a model quality perspective
As an emerging technique, Federated Learning (FL) can jointly train a global model with the
data remaining locally, which effectively solves the problem of data privacy protection …
data remaining locally, which effectively solves the problem of data privacy protection …
CLC: A consensus-based label correction approach in federated learning
Federated learning (FL) is a novel distributed learning framework where multiple
participants collaboratively train a global model without sharing any raw data to preserve …
participants collaboratively train a global model without sharing any raw data to preserve …
Federated learning for personalized humor recognition
Computational understanding of humor is an important topic under creative language
understanding and modeling. It can play a key role in complex human-AI interactions. The …
understanding and modeling. It can play a key role in complex human-AI interactions. The …
Federated Learning Client Pruning for Noisy Labels
Federated Learning (FL) enables collaborative model training across decentralized edge
devices while preserving data privacy. However, existing FL methods often assume clean …
devices while preserving data privacy. However, existing FL methods often assume clean …
Labeling chaos to learning harmony: Federated learning with noisy labels
Federated Learning (FL) is a distributed machine learning paradigm that enables learning
models from decentralized private datasets where the labeling effort is entrusted to the …
models from decentralized private datasets where the labeling effort is entrusted to the …