DearFSAC: An Approach to Optimizing Unreliable Federated Learning via Deep Reinforcement Learning

Unfortunately, conventional approaches pay little attention to most defects [Fung In federated learning (FL), model aggregation has et al., 2018]. Therefore, an efficient approach to alleviating been widely adopted for data privacy. In recent performance degradation caused by defective local models is years, assigning different weights to local models strongly needed for FL. Existing researches on blockchainbased has been used to alleviate the FL performance FL have defined the concept of reputation, which manifests degradation caused by differences between local the reliability of each local model [Kang et al., 2019] datasets. However, when various defects make the [Kang et al., 2020]. Similarly, we evaluate the model quality FL process unreliable, most existing FL approaches to measure how trustworthy a local model is. After learning expose weak robustness. In this paper, we propose about the quality of each local model, we are motivated to design the DEfect-AwaRe federated soft actor-critic a deep neural network (DNN) to assign optimal weights (DearFSAC) to dynamically assign weights to local to local models, so that the global model can maintain a considerable models to improve the robustness of FL. The deep performance no matter if there exist defects or not.