Empirical Convergence Analysis of Federated Averaging for Failure Prognosis

Abstract

Data driven prognosis involves machine learning algorithms to learn from previous failures and generate its prediction model. However, often a single asset does not fail so frequently to have enough training data in the form of historical failures. This problem can be addressed by learning from failures across a cluster of similar other assets, but often working in different environments. The algorithm therefore must learn from a distributed dataset which might be heterogenous but with underlying similarities. Federated Learning is an emerging technique that has recently also been proposed as a fitting solution for prognosis of industrial assets. However, even the most commonly used Federated Learning algorithms lack theoretical convergence guarantees, and therefore their convergence must be analysed empirically. This paper empirically analyses the convergence of the Federated Averaging (FedAvg) algorithm for a fleet of simulated turbofan engines. Results demonstrate that while FedAvg is applicable for prognosis, it cannot acknowledge the differences in asset failure mechanisms. As a result, the prognosis framework needs to be modified such that similar failures are clustered together before FedAvg can be implemented.