Distributed machine learning can give an adaptable but strong shared condition for the design of trusted AI applications; this is mainly due to lack of privacy of centralised remote learning mechanisms. This notwithstanding, also distributed approaches have been compromised by several attack models (mainly data poisoning): in such a situation, a malicious member of the learning party may inject bad data. As such applications are growing in criticality, learning models must face with security and protection just as with versatility issues. The aim of the paper is to improve these applications by providing extra security features for distributed and federated learning mechanisms: more in the details, the paper examines specific concerns such as the utilisation of blockchain, homomorphic cryptography and meta-modelling techniques to ensure protection as well as other non-functional properties.
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