Robust softmax aggregation on blockchain based federated learning with convergence guarantee. (arXiv:2311.07027v2 [cs.CR] UPDATED)

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 …

aggregation algorithms blockchain components convergence data data sets distributed federated federated learning guarantee local model training server sharing training