  Federate learning can conduct machine learning as well as protect the privacy
of self-owned training data on corresponding ends, instead of having to upload
to a central trusted data aggregation server. In mobile scenarios, a
centralized trusted server may not be existing, and even though it exists, the
delay will not be manageable, e.g., smart driving cars. Thus, mobile federate
learning at the edge with privacy-awareness is attracted more and more
attentions. It then imposes a problem - after data are trained on a mobile
terminal to obtain a learned model, how to share the model parameters among
others to create more accurate and robust accumulative final model. This kind
of model sharing confronts several challenges, e.g., the sharing must be
conducted without a third trusted party (autonomously), and the sharing must be
fair as model training (by training data)is valuable. To tackle the above
challenges, we propose a smart contract and IPFS (Inter-Planetary File System)
based model sharing protocol and algorithms to address the challenges. The
proposed protocol does not rely on a trusted third party, where
individual-learned models are shared/stored in corresponding ends. Conducted
through extensive experiments, three main steps of the proposed protocol are
evaluated. The average executive time of the three steps are 0.059s, 0.060s and
0.032s, demonstrating its efficiency.
