Exploring a Digital Bio-Marker (DBM) is challenging for detecting the progress of Parkinson's Disease (PD) since the unclear disease mechanism. Traditional clinical trials first formulate a DBM hypothesis and then proceed to verify them by control study, the process of which is often limited to clinicians' expertise and experience. Machine Learning with big data provides an opportunity to automatically discover DBMs, while recent privacy policies, such as GDPR, have created extra obstacles for collecting patient information and conducting multicenter trials. To address this issue, we propose a novel DBM discovery paradigm with federated learning, called FedDBM, which forms a closed loop consisting of model training and post hoc explanation. FedDBM employs a federated split learning to preserve patients' privacy in a multicenter clinical trial which attempts to build a model that maps signal data to PD progress. Then, a Federated Shapley Additive exPlanations method (FedSHAP) is proposed to find those features of vital importance in the well-trained model, known as DBM. The proposed FedDBM was evaluated on four PD typical motor symptoms and the extensive experimental results demonstrated that FedDBM showed comparable performance with SOTA federated learning methods, and the explored DBMs were proved to be more sensitive than current clinical metrics.