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Blockchain based federated learning is a distributed learning scheme thatallows model training without participants sharing their local data sets, wherethe blockchain components eliminate the need for a trusted central servercompared to traditional Federated Learning algorithms. In this paper we proposea softmax aggregation blockchain based federated learning framework. First, wepropose a new blockchain based federated learning architecture that utilizesthe well-tested proof-of-stake consensus mechanism on an existing blockchainnetwork to select validators and miners to aggregate the participants' updatesand compute the blocks. Second, to ensure the robustness of the aggregationprocess, we design a novel softmax aggregation method based on approximatedpopulation loss values that relies on our specific blockchain architecture.Additionally, we show our softmax aggregation technique converges to the globalminimum in the convex setting with non-restricting assumptions. Ourcomprehensive experiments show that our framework outperforms existing robustaggregation algorithms in various settings by large margins.

Robust softmax aggregation on blockchain based federated learning with convergence guarantee