Dual-Client Selection Algorithm Based on Model Similarity and Local Loss
Federated learning is a distributed machine-learning technique that collaboratively constructs a global model by aggregating local model parameters from clients.Existing client selection algorithms for federated learning perform only pre-or post-training.With statistically heterogeneous client data,pre-training selection algorithms may involve poorly performing clients in aggregation,leading to a reduction in model accuracy.However,post-training selection algorithms require that all clients participate in training,which results in significant communication overhead.To address these issues,this study proposes a Dual-Client Selection(DCS)algorithm.This algorithm first selects a subset of clients for training prior to the local training phase to reduce the download of global models.Following the subset training,some clients are chosen to participate in aggregation to reduce the upload of local models.Prior to local training,the server conducts hierarchical clustering based on the cosine similarity between the local and global models.This process yields different selection probability distributions from which an unbiased training subset is selected to better adapt to the statistical heterogeneity of the client data.Following subset training,the server considers not only the local loss but also the cosine similarity between the local and global models.This enables the aggregated subset to be chosen,thereby improving the accuracy of the global model.Experimental results on the Fashion-MNIST and CIFAR-10 datasets demonstrate that the proposed DCS algorithm improves the test accuracy by a maximum of 8.55 percentage points as compared with the baseline algorithm,where the communication overheads of the uplink and downlink are O(mn+2d)and O(dn+m),respectively.
federated learningclient selectionmodel similarityclusteringlocal loss
万方数据
维普
