FedREAS: A Robust Efficient Aggregation and Selection Framework for Federated Learning

Abstract

In the field of Natural Language Processing (NLP), Deep Learning (DL) and Neural Network (NN) technologies have been widely applied to machine translation and sentiment analysis and have demonstrated outstanding performance. In recent years, NLP applications have also combined multimodal data, such as visual and audio, continuously improving language processing performance. At the same time, the size of Neural Network models is increasing, and many models cannot be deployed on devices with limited computing resources. Deploying models on cloud platforms has become a trend. However, deploying models in the cloud introduces new privacy risks for endpoint data, despite overcoming computational limitations. Federated Learning (FL) methods protect local data by keeping the data on the client side and only sending local updates to the central server. However, the FL architecture still has problems, such as vulnerability to adversarial attacks and non-IID data distribution. In this work, we propose a Federated Learning aggregation method called FedREAS. The server uses a benchmark dataset to train a global model and obtains benchmark updates in this method. Before aggregating local updates, the server adjusts the local updates using the benchmark updates and then returns the adjusted benchmark updates. Then, based on the similarity between the adjusted local updates and the adjusted benchmark updates, the server aggregates these local updates to obtain a more robust update. This method also improves the client selection process. FedREAS selects suitable clients for training at the beginning of each round based on specific strategies, the similarity of the previous round’s updates, and the submitted data. We conduct experiments on different datasets and compare FedREAS with other Federated Learning methods. The results show that FedREAS outperforms other methods regarding model performance and resistance to attacks.