FedDRL: A Trustworthy Federated Learning Model Fusion Method Based on Staged Reinforcement Learning

arXiv:2307.13716v4 Announce Type: replace
Abstract: Traditional federated learning uses the number of samples to calculate the weights of each client model and uses this fixed weight value to fusion the global model. However, in practical scenarios, each client's device and data heterogeneity leads to differences in the quality of each client's model. Thus the contribution to the global model is not wholly determined by the sample size. In addition, if clients intentionally upload low-quality or malicious models, using these models …

abstract arxiv client cs.ai cs.lg data differences federated learning fusion global however leads practical reinforcement reinforcement learning samples trustworthy type value