  Blockchain based federated learning is a distributed learning scheme that
allows model training without participants sharing their local data sets, where
the blockchain components eliminate the need for a trusted central server
compared to traditional Federated Learning algorithms. In this paper we propose
a softmax aggregation blockchain based federated learning framework. First, we
propose a new blockchain based federated learning architecture that utilizes
the well-tested proof-of-stake consensus mechanism on an existing blockchain
network to select validators and miners to aggregate the participants' updates
and compute the blocks. Second, to ensure the robustness of the aggregation
process, we design a novel softmax aggregation method based on approximated
population loss values that relies on our specific blockchain architecture.
Additionally, we show our softmax aggregation technique converges to the global
minimum in the convex setting with non-restricting assumptions. Our
comprehensive experiments show that our framework outperforms existing robust
aggregation algorithms in various settings by large margins.
