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Data aggregation is a fundamental building block of modern distributed systems. Averaging based approaches, commonly designated gossip-based, are an important class of aggregation algorithms as they allow all nodes to produce a result, converge to any required accuracy, and work independently from the network topology. However, existing approaches exhibit many dependability issues when used in faulty and dynamic environments. This paper extends our own technique, Flow Updating, which is immune to message loss, to operate in dynamic networks, improving its fault tolerance characteristics. Experimental results show that the novel version of Flow Updating vastly outperforms previous averaging algorithms, it self adapts to churn without requiring any periodic restart, supporting node crashes and high levels of message loss.