To address the low accuracy of deep learning based fault diagnosis under distributed scenarios that caused by limited sample of single node and unbalanced distribution of working conditions of multiple nodes, et al, a multi-wavelet coefficients enhanced dynamic aggregation federal deep network ( MWCE-FedDWA) is proposed for fault diagnosis under multiple conditions with distributed small samples. A framework for fault diagnosis using MWCE-FedDWA is proposed, wavelet coefficient features are extracted by each terminal node from its local samples, a method based on multi-wavelet coefficient fusion in deep network is proposed for feature enhancement, each local model utilizes a set of diversified wavelet coefficients to extract more discriminative fault features. A global federal deep network model is constructed in aggregation node by aggregating the local models from multiple terminal nodes, and then adopted for fault diagnosis under multiple conditions. To reduce the influence of non-independent and identically distributed data among multiple nodes, a federated dynamic weighted aggregation algorithm is proposed to balance the contribution of local models. The results on bearing vibration data show that the proposed method can achieve high-precision diagnosis under multiple conditions with distributed small samples.