FedSS: A High-Efficiency Federated Learning Method for Semantic Segmentation

Federated learning is a distributed machine learning framework that allows multiple clients to collaborate on training global models without sharing raw data, thereby protecting data privacy. However, it is still a challenge to construct an efficient federated learning method for the semantic segmentation task of automated driving street view. On the one hand, the complexity of the semantic segmentation model is high, resulting in huge computing and communication overhead of client local training. On the other hand, the client data distribution is significantly different and has Non-Independent and Identically Distributed (non-IID) characteristics, which easily leads to the difficulty of global model convergence or the deterioration of generalization performance. Therefore, this paper proposes a Federal Street View segmentation method, Federal Street View Segmentation (FedSS), which optimizes model training by improving the cross-entropy loss function and designing a gradient compensation strategy and a gradient sparse compression strategy to alleviate the high communication overhead in federation learning. Extensive experiments show that our approach can consume fewer computational resources and achieve higher communication efficiency while improving semantic segmentation performance.