  Federated learning is a distributed machine learning technology, which
realizes the balance between data privacy protection and data sharing
computing. To protect data privacy, feder-ated learning learns shared models by
locally executing distributed training on participating devices and aggregating
local models into global models. There is a problem in federated learning, that
is, the negative impact caused by the non-independent and identical
distribu-tion of data across different user terminals. In order to alleviate
this problem, this paper pro-poses a strengthened federation aggregation method
based on adaptive OPTICS clustering. Specifically, this method perceives the
clustering environment as a Markov decision process, and models the adjustment
process of parameter search direction, so as to find the best clus-tering
parameters to achieve the best federated aggregation method. The core
contribution of this paper is to propose an adaptive OPTICS clustering
algorithm for federated learning. The algorithm combines OPTICS clustering and
adaptive learning technology, and can effective-ly deal with the problem of
non-independent and identically distributed data across different user
terminals. By perceiving the clustering environment as a Markov decision
process, the goal is to find the best parameters of the OPTICS cluster without
artificial assistance, so as to obtain the best federated aggregation method
and achieve better performance. The reliability and practicability of this
method have been verified on the experimental data, and its effec-tiveness and
superiority have been proved.
